The purpose of this work is to compare the ways in which relational databases—more especially, Microsoft SQL Server—and non-relational databases—MongoDB—handle unstructured data in JSON format. Although a lot of study has been done comparing different database management systems in terms of security, performance, and other aspects, little is known about how these databases are evaluated according to the type of data they handle. The main goal of this study is to examine the various features that relational and non-relational databases (...) provide for handling JSON data. We will conduct a number of tests to do this, making sure that the relevant data doesn't need to be normalised. To draw a conclusion, the outcomes of these studies will thereafter be examined. (shrink)

Businesses and people outsource database to realize helpful and low-cost applications and administrations. In arrange to supply adequate usefulness for SQL inquiries, numerous secure database plans have been proposed. In any case, such plans are helpless to protection leakage to cloud server. The most reason is that database is facilitated and handled in cloud server, which is past the control of information proprietors. For the numerical extend inquiry (“>”, “<”, etc.), those plans cannot give adequate protection security against (...) viable challenges, e.g., security spillage of measurable properties, get to design. Besides, expanded number of questions will definitely spill more data to the cloud server. In this paper, we propose a two-cloud engineering for secure database, with a arrangement of crossing point conventions that give security conservation to different numeric-related extend questions. Security analysis shows that privacy of numerical information is strongly protected against cloud providers in our proposed scheme. (shrink)

This paper describes an Intelligent Tutoring System for helping users with ADO.NET called ADO-Tutor. The Intelligent Tutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO.NET, the technology used by Microsoft.NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to different difficulty (...) level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system. (shrink)

In 2013, I've transformed the each_co_uk server side system (aspx / c#) to the client system of a few javascript files ≈400KB potentially webassembly, without plugins / 3rd party / css.. The system maximally reuses functions & data to apply: 1) Linguistic principle of arbitrariness of the sign maximizing repetitions, 2) Multi layer logic of the chess composition, 3) Information Theory minimizing entropy. Some of these ideas were presented at conferences in Santorini, Adelaide, Geneva, Daejon and virtually. Philosophically, any (...) system only organizes the existing reality (information), but can't create it. Its fragmentation easily leads to illusions (entropy): e.g. Zeno's Achilles can't overtake the slower tortoise because as he gets closer, the tortoise gets a bit further, so he gets closer and closer but never reaches her. So the more are logic and data fragmented in various layers (server, javascript, css, sql, frameworks..), the less capable system is. (shrink)

Server-less computing, also known as Function as a Service (FaaS), revolutionizes cloud architecture by allowing developers to focus on code and functionality without managing underlying infrastructure. This paradigm enhances resource management efficiency by dynamically allocating resources only when needed, thus optimizing cost and performance. Server-less models, epitomized by platforms like AWS Lambda, Google Cloud Functions, and Azure Functions, provide automatic scaling and fine-grained billing, making them ideal for applications with variable workloads. While challenges such as cold start latency (...) and complex debugging exist, the benefits of server-less computing—including cost savings, simplified deployment, and accelerated innovation—make it a transformative approach in the cloud computing landscape. This paper explores the principles, benefits, challenges, and practical applications of server-less computing in modern cloud architectures, highlighting its potential to drive efficiency and innovation. (shrink)

With the exponential growth of digital content, unstructured data has become a dominant force in today's data landscape, accounting for nearly 80% of enterprise information. Traditional relational database management systems (RDBMS), governed by rigid schemas and ACID compliance, were not originally designed to handle the dynamic and heterogeneous nature of unstructured data such as images, videos, logs, emails, and social media feeds. In contrast, NoSQL databases have emerged as a viable alternative, offering flexible schema designs, high scalability, and optimized performance (...) for various unstructured and semi-structured data workloads. (shrink)

Securing sensitive files stored on cloud servers presents a paramount challenge in today's digital landscape, demanding a robust file auditing mechanism that transcends traditional methods. This paper introduces an innovative approach to file auditing in cloud environments, distinguished by the integration of secure download key notifications with login processes. Unlike conventional file auditing techniques that primarily focus on monitoring file activities, this proposed solution recognizes the critical need for securely tracking file downloads. To address this gap, we present a sophisticated (...) system that not only comprehensively audits file activities but also seamlessly integrates with login processes to provide secure download key notifications. By bridging the gap between file auditing and user authentication, our solution enhances the security posture of cloud-based file storage systems, empowering organizations to safeguard their sensitive data effectively. Through detailed analysis and evaluation, we demonstrate the efficacy and practicality of our approach in enhancing the security and integrity of sensitive files stored on cloud servers. (shrink)

In this article, we reimagine the Internet as a feminist cosmos called a Feminist Server Stack. By scrutinising how webserver infrastructure is normalised based on imperial conventions and distorted knowledge systems, we suggest alternatives. This embrace of ‘post-patriarchal futures’ (Bardzel 2019, 20) offers counter perspectives on how we may holistically embody the Internet as a situated ecology. When experiencing the Internet materiality, it leads us to imagine beyond the known and possible. We focus on enchanting affirmations and tacit hands-on (...) computational methods from the frontiers of feminist digital literacy and server communities. These acts make visible the seams and fissures of concealed undercurrents and schemata of infrastructures. The findings reveal utterly different relationships between webserver providers and clients. Drawing from extensive fieldwork and observational analysis, we show how the choreography of happenings is seeding the ground for participatively co-designing the transfiguration of planetary computing into one that is scalable, visceral, and restorative. We welcome these nascent pathways to guide us to an enigmatic realm upon which theories of co-design could dance in communion with a realm that is to-come. (shrink)

This project presents an innovative approach to the efficient enumeration of hidden service URLs (.onion) on the TOR network using a custom-built graphical user interface (GUI) application. The tool leverages web scraping techniques, targeting the Ahmia search engine, to retrieve and analyze active hidden server URLs. Through the integration of Python’s Tkinter library for GUI development and the use of requests and regular expressions for content mining, the application simplifies the process of discovering and visualizing .onion URLs. The tool (...) allows users to input search queries, clean them for web compatibility, and display the results in real time. Additionally, the application provides graphical insights into the search results by generating visual data representations using matplotlib. This paper discusses the architecture, development process, and functionality of the tool, providing a new method for researchers to explore the TOR network. (shrink)

Existing systems often lack a centralized platform for managing server and firewall operations, leading to fragmented oversight and delayed responses to cyber threats. This gap frequently results in increased vulnerabilities and operational inefficiencies within organizations. To address this issue, we propose the development of the Cybersecurity Portal, designed to provide real-time monitoring, predictive analytics, and streamlined resource allocation for server and firewall management. The portal will offer comprehensive insights into system performance, potential vulnerabilities, and proactive threat detection. By (...) equipping administrators with timely and relevant data, this platform aims to empower organizations to make informed security decisions and implement effective risk mitigation strategies. Ultimately, the Cybersecurity Portal seeks to enhance the overall cybersecurity posture of organizations, ensuring better protection of their digital assets against evolving threats. (shrink)

In the contemporary landscape of public procurement, organizations face increasing complexities in managing procurement and vendor payment processes. This project focuses on developing a real-time procurement and vendor payment management system for North Eastern Electric Power Corporation Limited (NEEPCO), leveraging modern web technologies, including React, Node.js, Express, and SQL. The proposed system aims to centralize and automate the procurement lifecycle, enhancing operational efficiency, transparency, and compliance with government regulations. By integrating with the Government e-Marketplace (GeM), the system facilitates seamless procurement (...) from Micro and Small Enterprises (MSEs), promoting inclusivity and economic participation. The procurement data portal will provide real-time tracking of all procurement activities, from requisition to delivery, while automating workflows to reduce manual intervention and errors. The vendor payment portal will streamline payment processes, ensuring timely payments and realtime updates for vendors, thus fostering stronger relationships. Additionally, advanced analytics will be integrated into the system, enabling data-driven decision-making and improved financial management. The expected outcomes include reduced procurement cycle times, improved compliance, and enhanced vendor satisfaction. By addressing the challenges inherent in traditional procurement methods, this project will contribute significantly to NEEPCO's operational efficiency. Ultimately, the system aims to set a benchmark for procurement practices in the public sector, reinforcing the importance of adopting modern technologies to meet the demands of a dynamic marketplace. (shrink)

Web attacks, such as SQL injection, cross-site scripting (XSS), and distributed denial-ofservice (DDoS), pose significant threats to the security and integrity of online systems. Traditional detection methods, relying on rulebased systems or shallow machine learning, often struggle to keep pace with the evolving sophistication of these attacks. This paper proposes an end-to-end deep learning framework for detecting web attacks, leveraging the power of neural networks to automatically learn complex patterns and features from raw web traffic data. Unlike conventional approaches that (...) require extensive manual feature engineering, our method processes unprocessed HTTP request logs and network traffic directly, enabling a fully automated detection pipeline. The proposed model employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both spatial and temporal dependencies in web traffic data. CNN layers extract local patterns, such as malicious payloads or anomalous headers, while RNN layers, enhanced with attention mechanisms, model sequential behaviors indicative of attacks. The framework is trained endto-end on a diverse dataset comprising benign traffic and real-world attack samples, ensuring robustness across various attack vectors. Preprocessing is minimized to tokenization and normalization, preserving the raw data's integrity and reducing human intervention. Evaluation results demonstrate that the model achieves superior performance compared to traditional methods, with an accuracy of over 95% and a low false-positive rate. It effectively identifies both known and zero-day attacks by generalizing learned representations. Additionally, the system scales efficiently, processing large volumes of traffic in real-time, making it suitable for deployment in production environments. This end-to-end deep learning approach not only enhances detection accuracy but also adapts to emerging threats, offering a proactive solution to the growing challenge of web security. Future work will focus on integrating explainability to improve trust and interpretability in security operations. (shrink)

Can the theory that reality is a simulation be tested? We investigate this question based on the assumption that if the system performing the simulation is nite (i.e. has limited resources), then to achieve low computational complexity, such a system would, as in a video game, render content (reality) only at the moment that information becomes available for observation by a player and not at the moment of detection by a machine (that would be part of the simulation and whose (...) detection would also be part of the internal computation performed by the Virtual Reality server before rendering content to the player). Guided by this principle we describe conceptual wave/particle duality experiments aimed at testing the simulation theory. (shrink)

I aim to develop a tool for comparing theories of vagueness, using Structured Query Language. Relevant SQL snippets will be used throughout.

Gathering and analyzing enormous volumes of data is known as "big data," and it includes information from users, sensors, healthcare providers, and companies. Using the Hadoop framework, large amounts of data are stored, managed, and dispersed across multiple server nodes. Big Data issues, including security holes in the Hadoop Distributed File System (HDFS), the architecture's core layer, are highlighted in this article. The methodology includes setting up a Hadoop environment, integrating Kerberos for authentication, enabling HDFS encryption zones, implementing SSL/TLS (...) for data in transit, and utilizing Apache Ranger and Apache Knox for access control and perimeter security, respectively. The results demonstrate the successful implementation of all planned security measures, achieving a robust security framework for the Hadoop cluster. Performance testing indicates a 10% reduction in processing speed due to the security features, a trade-off deemed acceptable given the significant enhancement in data protection. Compliance testing confirms adherence to GDPR and CCPA regulations, ensuring legal and secure data management. Overall, the study underscores the feasibility of integrating comprehensive security measures within a Hadoop environment, balancing the need for robust data protection with minimal performance impact. Future work includes optimizing security configurations to further mitigate performance degradation and exploring advanced security measures for enhanced threat detection and response. This methodology provides a scalable and secure solution for managing large datasets in compliance with global data protection standards. (shrink)

Web applications are increasing day by day and the choice of technology for building a web application is a tedious task. A good website needs to be scalable, functional, responsive, reliable, secure etc. Therefore, comparing the different trending technologies for web design is crucial. The entire design of our website requires the selection of the programming languages. It is difficult to give a quick solution to this issue without first considering what our website will do both now and in the (...) future. One of the initial decisions to be made is whether the website will be static, interacting with users relatively little, or dynamic, displaying personalised information to users in response to interactions. Also, there are many stacks trending and available in the market for web development like MERN stack, MEAN stack, MEVN stack or ruby on rails or DERN technology. It's very necessary to understand all, the key differences between them and which one is right to choose when. This paper attempts to assist web developers through conducting an empirical comparison of the most significant web-based programming languages and databases. The paper compares frontend technologies like React and Angular based on certain parameters like DOM, architecture and data binding, in each case comparing their performance and also do the performance analysis of backend technologies like MongoDB and PostgreSQL, which are no-SQL and SQL databases respectively, based on e-commerce use case application. The paper finally suggests which technology to choose for which use case and under what conditions and requirements. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to provide granular, context-aware insights. This research (...) introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

Moral dilemmas can arise from uncertainty, including uncertainty of the real values involved. One interesting example of this is that of experimentation on human embryos and foetuses, If these have a moral stauts similar to that of human persons then there will be server constraitns on what may be done to them. If embryous have a moral status similar to that of other small clusters of cells, then constraints will be motivated largely by consideration for the persons into whom (...) the embryos may develop. If the truth lies somewhere between these two extremes, the embryo having neither the full moral weight of persons, nor a completely negligible moral weight, then different kinds of constraints will be appropriate. On the face of it, in order to know what kinds of experiements, if any, we are morally justified in performing on embryos we have to know what the moral weight of the embryo is. But then an impasse threatens, for it seems implausible that we can settle with certainty the exact moral status of the human embryo. It is the purpose of this paper to show that moral uncertainty need not make rational moral justification impossible. I develop a framework which distinguishes between what is morally right/wrong, and what is morally justified/unjustified, and applies standard decision theoretic tools to the case of moral uncertainties. (This was the first published account of what has subsequently become known as Expected Moral Value Theory. An earlier version of the paper, "A decision theoretic argument against human embryo experimentation", was published in M. Fricke (ed.), Essays in honor of Bob Durrant. (University of Otago Press, 1986) 111-27.). (shrink)

The Automated Data Analytics System leverages Generative AI, specifically OpenAI’s GPT-4, to empower non-technical users to interact with enterprise data through natural language queries. Seamlessly integrated with a Snowflake data warehouse and built on a Streamlit-based user interface, the tool automatically translates user input into SQL queries, executes them, and delivers structured insights along with dynamic visualizations. This eliminates the need for manual SQL writing and custom development, significantly lowering the technical barriers traditionally associated with data analysis. The intuitive interface (...) enables business analysts and decision-makers to independently explore data, identify trends, and generate real-time reports without relying on developers or data teams. By automating the entire analytics workflow from natural language query generation to result visualization the platform simplifies data exploration, enhances user autonomy, and fosters a data-driven culture across the organization. As a result, businesses can make faster, smarter decisions and extract actionable insights from raw data with minimal effort and no coding required. (shrink)

Application of the reliability of repairable systems on solving problems from constructing production systems takes an important place in the process of finding the optimal solution among the suggested system choices. The basic hypothesis when using the reliability of the repairable systems is that every machine is representing a component, a fact that is debatable when talking about technical sciences. However, considering the second assumption of the stationary process, the function of the availability is introduced. It represents the measure between (...) the reliability and the benefit of maintaining the components and the system as a whole one. An additional and the last reason for analyzing constructing machine as a whole one has an economic reason. From the aspect of the total cost and the capacity of the system, the function of the availability is catching more and more impact in the process of determinating the total cost in bill of quantities as the most important part of tender documents. Until former works, techno-economic optimisation usually ran over when choosing the system of the construction machines. In the work, there are shown the methodologies for gathering and analyzing the indicators of availabilities per unit of a company's machine park. The time of demission and reparation have been investigated among 34 components in the period of 6 years. Based on the obtained result for different conditions of machine exploitation and facilities, it is suggested the methodology of the future behaviour of the corresponding within the model optimization availability of constructing production system with the aim of eliminating extra criterion. The suggested model takes in consideration the total cost of the components when they are functional, and when they are not. This kind of approach enables the one who take choices decision of the height of profits, in the lenght of work in function of the projection of cash flow and managing with real indicators of functioning the system at the project level. Given the fact that the maintanance policy of machines is based on availability, and not on reliability of components, with the aim of maximizing the availability of different budget scenarios, a logical approach to the selection of the objective function in the terms of management of construction production systems is to minimize costs for a given availability of system constructing machinery is based on the use of historical data base with the values of the operational availability of components, conditions in which they function, in the process of forecasting the project availability for projects that follow. Ranking variant solutions is the result of the adopted function of the aim as a consequence due to enlargement methods of availability for serial parallel systems with the associated restrictions. For optimal system availability is taken a value that belongs to minimal total cost. However, it is predicted that after the end of the financial year, the real and projected indicators of system functions get compared to each other. Informational support to the proposed model is informational company system, which was built in the PHP programming language on the Linux software platform, and with data based in My SQL. The suggested model is tested on the system for the production and embedding bitumen bound materials on the specific project. Obtained results confirm the assumptions used in work. (shrink)

Abstract: The Spotify Status Dataset is a valuable resource that provides real-time insights into the operational status and performance of Spotify, a popular music streaming platform. This dataset contains a wide array of information related to server uptime, user activity, service disruptions, and more, serving as a critical tool for both Spotify's internal monitoring and the broader data analysis community. As digital services like Spotify continue to play a central role in music consumption, understanding the platform's status becomes crucial (...) for ensuring a seamless user experience. This dataset offers numerous research and analysis opportunities, whether you're interested in exploring service availability trends or investigating user behavior patterns during service disruptions. This study highlights the significance of the Spotify Status Dataset, its potential applications, and the valuable insights it can offer to various stakeholders, from Spotify engineers working to enhance service reliability to researchers studying the effects of downtime on user engagement. It invites readers on a journey through this dataset to uncover the secrets of Spotify's operational status and its impact on music enthusiasts worldwide. (shrink)

As the internet continues to evolve, the security of web applications has become a paramount concerns for businesses and individuals alike. The ”Website Vulnerability Scanner” project is designed to address this challenge by developing a comprehensive tool that automat-can detect, analyze, and report potential security vulnerabilities on websites. This scanner focuses on identifying a wide range of common web application vulnerabilities, including but not limited to SQL injection, cross-site scripting (XSS), cross-site request forgery (CSRF), broken authentication, insecure file uploads, and (...) security mis-configurations. The scanner leverages both active and passive scanning techniques to detect weaknesses by simulating potential attacks and analyzing a website’s response. It integrates with popular vulnerability databases such as OWASP and CVE, ensuring that the tool stays to date with the latest threats. The tool generates detailed reports with identified vulnerabilities, risk levels, and actionable recommendations for mitigation, aiding developers to strengthen the web security. This project is developed using Python, incorporating libraries such as ‘requests‘, ‘Beautiful Soup‘, and ‘nmap‘ for network scanning, and is built with modularity and scalability in mind. The tool aims to be user-friendly, making it accessible to both web developers and security analysts. By automating the vulnerability detection process, the project contributes to the overall enhancement of Web application security in a proactive manner. (shrink)

At the beginning of the COVID-19 pandemic, high hopes were put on digital contact tracing, using mobile phone apps to record and immediately notify contacts when a user reports as infected. Such apps can now be downloaded in many countries, but as second waves of COVID-19 are raging, these apps are playing a less important role than anticipated. We argue that this is because most countries have opted for app configurations that cannot provide a means of rapidly informing users of (...) likely infections while avoiding too many false positive reports. Mathematical modelling suggests that differently configured apps have the potential to do this. These require, however, that some pseudonymised data be stored on a central server, which privacy advocates have cautioned against. We contend that their influential arguments are subject to two fallacies. First, they have tended to one-sidedly focus on the risks that centralised data storage entails for privacy, while paying insufficient attention to the fact that inefficient contact tracing involves ethical risks too. Second, while the envisioned system does entail risks of breaches, such risks are also present in decentralised systems, which have been falsely presented as ‘privacy preserving by design’. When these points are understood, it becomes clear that we must rethink our approach to digital contact tracing in our fight against COVID-19. There are no data in this work. (shrink)

In 1995, as a student of psychology inspired by natural science, I defined a logical model of personality explaining psychosis. I created (for my MA thesis, 1998 and grant research, 1999) new kind of tests assessing intelligence, creativity, prejudices, expectations to show more exact methods in psychology. During my Phd study in economics, I developed 'Maximization of Uniqueness (Originality)' model enhancing the classic utility to explain irrational motivations linking economics and psychology. Later I became computer programmer developing functional programming. According (...) to Personality model (and my experience in chess composition), I've built server and client logic of information system www.each.co.uk, and produced visual art. I presented the excerpts of 'Personality Model' in a few articles, and later as part of my art exhibitions: From Animation (Oct 2013, Holland Park, London), Parallax 12 (Feb 2015, Chelsea Town Hall, London), Fading Memory (Sep 2015, Weißenohe / Nuremberg, Germany). Another work: The Science is a subset of the Art extends Personality Model to art, society. At the end I add example and explanation of a special problem, when the higher intelligence leads to a wrong solution. It was published in Japanese Journal Problem Paradise in 1999. All presented results can be repeated. Computer testing would enable to acquire more data and more exact analysis. (shrink)

This project is a robust online education platform designed to streamline learning and teaching experiences. It integrates features for course management, user authentication, and interactive learning. Core functionalities include user registration, tutor and student profile management, course search and enrollment, video content streaming, and interactive elements like bookmarks, comments, and likes to enhance user engagement. The backend, powered by PHP, ensures dynamic content delivery, while the frontend employs CSS and JavaScript for a user-friendly interface. The database, structured using SQL, efficiently (...) handles data related to courses, users, and interactions. This platform is scalable and adaptable, making it ideal for educational institutions or independent tutors aiming to offer seamless virtual learning experiences. (shrink)

The Chandrapur Police Website is a secure, web-based platform aimed at improving communication between the public and law enforcement in Chandrapur, Maharashtra. It provides essential services like online crime reporting, safety tips, emergency contacts, missing persons updates, and real-time alerts.Built with PHP, MySQL, HTML, CSS, JavaScript, and AJAX, the system supports officer management, complaint tracking, and live updates. Security is ensured through input validation, secure authentication, session management, and CSRF protection, safeguarding sensitive data from threats like SQL injection and unauthorized (...) access. This platform enhances transparency, public safety, and easy interaction between citizens and the police. (shrink)

Natural disasters such as earthquakes, floods, wildfires, and landslides pose significant threats to human life, property, and infrastructure. Early detection and timely response are critical in minimizing the damage caused by such events. This paper presents the design and implementation of an IoT-based real-time disaster detection and alert system that leverages a network of low-cost sensors, microcontrollers, and cloud connectivity to monitor environmental parameters and detect potential disasters. The proposed system integrates various IoT sensors— including temperature, humidity, water level, gas, (...) vibration, and smoke sensors—to detect abnormal conditions indicative of natural hazards. Data collected from the sensors are transmitted to a cloud server via Wi-Fi or GSM modules, where it is processed and analyzed in real time. Once a threshold is crossed or a disaster is detected, the system triggers instant alerts via SMS, email, and mobile notifications to relevant authorities and users in the affected region. The system is designed to be modular, scalable, and energy-efficient, making it suitable for deployment in both urban and remote areas. A prototype has been developed and tested under simulated disaster scenarios to validate its effectiveness, response time, and reliability. The results demonstrate that the system can serve as an efficient early warning mechanism, contributing to disaster preparedness and risk reduction through the integration of smart technology. (shrink)

In today’s digital era, effective financial management is paramount for both individuals and businesses to ensure financial stability, informed decision-making, and long-term growth. The Expense Tracker project is designed as a comprehensive solution to streamline the process of tracking, managing, and analyzing expenditures. This system provides users with a structured, automated, and user-friendly platform to monitor their financial activities efficiently. By allowing users to record transactions, categorize expenses, set budgetary limits, and generate insightful spending reports, the application empowers them to (...) make informed financial decisions in real time. The Expense Tracker is built using React.js for an interactive and responsive front-end experience, Spring Boot for robust and scalable backend processing, and SQL for efficient and secure database management. This technology stack ensures seamless performance, secure data handling, and a smooth user experience. The system aims to eliminate manual errors associated with expense tracking while offering advanced analytics that provide valuable insights into spending habits. By leveraging data visualization and categorization techniques, users can identify unnecessary expenditures, optimize their budgets, and work toward financial goals more effectively. One of the key objectives of this project is to enhance financial planning by offering real-time analytics and notifications to help users stay within their budget limits. Additionally, the application is designed to be scalable and future-ready, with potential integrations of AI-driven financial insights to offer predictive expense recommendations and banking API integrations to automate transaction logging. These features will further enhance the system’s ability to assist users in maintaining financial discipline and improving their overall financial health. The Expense Tracker contributes to the rapidly evolving FinTech industry by delivering an innovative, intuitive, and secure financial management solution. By equipping users with comprehensive tools to oversee their financial activities, the application fosters better financial awareness, reduces overspending, and helps users gain full control over their monetary decisions. With a commitment to continuous improvement and adaptation to emerging financial technologies, this project serves as a stepping stone toward a more intelligent, automated, and data-driven financial ecosystem. (shrink)

An assessment of the extent of the use of electronic platforms by African academic staff in universities to disseminate research was done in this study. The study is informed by the growing importance of online repositories and preprint servers in the scientific communication of scholarly output, especially in an era where the use of metrics for research appraisals and funding decisions is commonly practised. The quantitative research method was adopted, based on the descriptive survey research design. The snowball sampling technique (...) was used for data collection. Data were collected from 1,977 respondents, distributed across 24 African countries, through the use of an electronic survey. There was a high rate of willingness among universities’ academic staff in Africa, to adopt various online platforms for research dissemination; ResearchGate is currently utilized the most for research dissemination, but Google Scholar is the platform respondents are more willing to adopt for research dissemination; the rate at which academic staff research output can be found online as a ratio of their total publication is 64.04% and in the ratio of 2.00:3.12; poor access to Internet facilities at home and workplaces are the major challenges academic staff face in the utilization of digital platforms for RD. It was recommended, amongst others that academic staff in universities should endeavour to explore and utilize at least ten of the online platforms mentioned in this study, to enable them disseminate their scholarly works to a wider audience and for increased visibility Keywords:. (shrink)

This project centers on developing a geo-tagging and monitoring system for plantations within the catchment area of a hydro project. Designed for the forest department, it provides real-time insights into environmental conditions to support the sustainable management of forest resources. The system comprises an Arduino Uno microcontroller that integrates GPS, DHT11 (temperature and humidity sensor), and LoRa modules to track and communicate critical data, such as the geographic location, temperature, and humidity around each plantation site. Powered by a 7.4V Li-ion (...) battery and regulated through an LM7805 voltage regulator for consistent 5V output, the system is optimized for low power consumption to ensure longevity in remote settings. Using the LoRa module, the system transmits data over long distances to a central receiver, which could be stationed at a monitoring hub. This data transmission operates independently of internet connectivity, making it ideal for isolated forest areas where network coverage is limited or non-existent. Each plant or designated plantation area can be geo-tagged and monitored through data collected by the sensors and sent through LoRa communication. The data is then processed and can be visualized on a server or application interface, giving forest officials an overview of the environmental conditions in each monitored area. This innovative system offers a scalable solution for remote environmental monitoring, allowing for expansion with additional sensors or wider deployment across forest areas. Furthermore, it lays a foundation for enhanced forest management through features like environmental alerts and trend monitoring, potentially aiding in conservation efforts by ensuring optimal conditions for plantation growth. By leveraging IoT technology in an eco-friendly, power-efficient setup, this project presents a modern approach to sustainable forestry and plantation monitoring. (shrink)

E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. An (...) established validation and management system could face a single failure point and the system is prone to Distributed Denial of Service Attacks (DDoS), denial of service attacks (DoS), malware and other cyber attacks. The execution of a privacy preserving and a secure decentralized system is enabled by the block chain technology. Here any third-party organizations do not have any control over the transactions of the Government. With the help of block chain technology, new and existing data are encapsulated within ledger or blocks, which are evenly distributed through the network in an enduring and sustainable way. The privacy and security of information are improved with the help of block chain technology, where distribution and encryption of data are performed through the total network. This analytical paper maps out the analysis of the security in the e-government system, utilizing the block chain technology that provides privacy and security of information and thereby enhancing the trust among the public sector. Qualitative and theoretical analysis is made for the proposed topic and implications of privacy and security of the proposed system is made. (shrink)

The advent of cloud computing has revolutionized the way organizations manage and deploy their IT infrastructure. Cloud services offer flexibility, scalability, and cost-efficiency, but they also consume a substantial amount of energy and contribute to environmental concerns. This abstract provides an overview of the research conducted on energy-efficient resource utilization in the cloud, focusing on strategies to reduce energy consumption while maintaining optimal performance and resource utilization. The primary objective of this research is to address the growing concern of the (...) environmental impact of cloud computing by proposing and evaluating various energy-efficient techniques and strategies. These strategies encompass multiple layers of the cloud infrastructure, including data centers, virtualization technologies, workload management, and resource allocation. In data centers, we explore techniques for optimizing cooling systems, power distribution, and server hardware, all aimed at reducing energy consumption. Virtualization technologies are a key component in cloud computing, and we investigate methods for efficient virtual machine placement, consolidation, and migration to make better use of the available resources and minimize energy consumption. Workload management strategies involve the dynamic allocation of resources to workloads based on demand. We propose intelligent resource allocation policies that consider factors such as workload characteristics, energy profiles of hardware components, and Quality of Service (QoS) requirements. These policies help in achieving an optimal trade-off between performance and energy consumption. (shrink)

The "Malware App Detection System" project enhances mobile security by detecting malicious apps through machine learning (ML) analysis of app behaviours. It examines behaviours like system calls (requests apps make to the operating system), permissions (levels of access apps have), and network connections (internet interactions). This focus on behaviour, rather than relying on specific malware signatures, enables the system to identify both known and new threats. The detection system is built using a dataset of both benign (harmless) and malicious apps, (...) with each app’s behaviour categorized and labelled. By analysing system calls, the system identifies unusual requests, which may indicate malicious intent. Permissions are also reviewed, as apps that seek abnormal access levels, like to contacts or storage, might have harmful intentions. Network connections are monitored to detect suspicious activities, such as connections to unauthorized servers, which could indicate data theft. To achieve this, ML models like Random Forest are trained on these behaviour patterns, learning to distinguish between safe and harmful apps. Once deployed, the system provides real-time detection, alerting users if it finds potentially dangerous activities. This proactive, behaviour-based approach protects users from unauthorized access and data leaks, adapting to new malware types as they arise. In doing so, the project offers a flexible and effective solution to the evolving challenges of mobile security. (shrink)

Ancient Greek philosophers were the first to postulate the possibility of explaining nature in theoretical terms and to initiate attempts at this. With the rise of monotheistic religions of revelation claiming supremacy over human reason and envisaging a new world to come, studies of the natural order of the transient world were widely considered undesirable. Later, in the Middle Ages, the desire for human understanding of nature in terms of reason was revived. This article is concerned with the fundamental reversal (...) of attitudes, from “undesirable” to “desirable”, that eventually led into the foundations of modern science. One of the earliest, most ingenious and most interesting personalities involved was Eriugena, a theologian at the Court of Charles the Bald in the 9th century. Though understanding what we call nature is only one of the several aspects of his theological work, his line of thought implies a turn into a pro-scientific direction: the natural order is to be understood in abstract terms of ‘primordial causes’; understanding nature is considered to be the will of God; man encompasses the whole of creation in a physical as well as a mental sense. Basically similar ideas on the reconciliation of scientific rationality and monotheistic religions of revelation were conceived, independently and nearly simultaneously, by the Arab philosopher al-Kindi in Bagdad. Eriugena was more outspoken in his claim that reason is superior to authority. This claim is implicit in the thought of Nicholas of Cusa with his emphasis on human mental creativity as the image of God’s creativity; and it is the keynote of Galileo’s ‘Letter to the Grand Duchess Christina’ some 800 years later, the manifesto expressing basic attitudes of modern science. -/- This article in English is based on the monography (in German): A. Gierer “Eriugena, al-Kindi, Nikolaus von Kues - Protagonisten einer wissenschaftsfreundlichen Wende im philosophischen und theologischen Denken”, Acta Historica Leopoldina 29 (1999), Barth Verlag in MVH Verlage Heidelberg, ISBN: 3-335-00652-6, which is also available online on this server. -/- -/- . (shrink)

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be able to resist a (...) wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments. (shrink)

The rapid digitalization of various societal sectors in Ukraine, including public services, banking, e-learning, and logistics, emphasizes the need for enhanced practical IT competencies among students. Despite the widespread availability of digital devices, educational curricula often lack tasks that provide hands-on experience with configuring networks and operating systems. This study aimed to address this gap by updating the curriculum of the "Information Processes and Methods of Their Algorithmization" course to focus on practical tasks that involve creating and configuring information resources. (...) To achieve this, the Debian operating system was chosen due to its flexibility, low resource requirements, and compatibility with outdated hardware. Key tasks included configuring services for business and home use, such as remote server access, secure file sharing, web hosting, and office environments with essential software like LibreOffice and Thunderbird. Performance metrics, including memory usage and disk space utilization, were evaluated for different configurations of the operating system, demonstrating its efficiency even on low-resource systems. The results highlight Debian as a cost-effective and adaptable solution for both educational and small-scale business needs, fostering practical IT skills while minimizing hardware expenses. In comparison, Ubuntu Server was found to be more advanced yet resource-intensive, making it better suited for corporate environments. This study underscores the potential of open-source solutions like Debian for improving IT education and promoting their broader adoption in academic and business contexts. (shrink)

This paper has been designed to develop the user-friendly server log management and monitoring system for the educational portal in an endeavor to enhance its capabilities. Server log management assumes a primary role in the current digital world in the maintenance of the health of the system, in the diagnosis of problems, and in ensuring that accountability is maintained. Current practice in log management is usually disjointed and does not have advanced mechanisms capable of handling the complexities of (...) modern systems. Our approach consolidates log information collection from the source thus coming through operating systems, applications, services, and devices on the network using Logstash to collect much easier log data. All log data collected will be structured in Elasticsearch with compression and archiving, which allows for long-term storage and minimizes the costs to the consumer. This means that users will be able to make more precise queries and filter log entries for analyzing the same in terms of time, log level, source, and keywords. In order to improve responsiveness to issues of critical concern, Kibana will be configured to send alerts based on predefined rules and provide insights through the use of charts and dashboards. All of these capabilities will allow them to determine trends, build reports, and support auditing and compliance. A high priority for the project was accessibility. We shall have an intuitive system: easy to implement, easy to use, for the target user groups intended here with minimum technical knowledge on their part, well supported by documentation. Deliverables expected would include a server log management system tuned for the Educational portal framework, which would improve system performance and security monitoring, real- time incident response, and operational efficiency through automating log classification. Finally, the project will implement Educational portal with an all-around effective tool for log management that can aid in efficient governance and security along with compliance to regulatory standards. Of course, our system will use the ELK stack to make it scalable and flexible enough to be ready for the unique demands of Educational based applications. (shrink)

To the Editor: In the rapidly changing world of scientific publication, the new system is complex, and there are many changes.1 Many variables, such as current trends, challenges, and technological developments, lead to various changes in the processes or contents of scientific publications. Understanding these changes is crucial for academic productivity. In contrast, some of these changes may have positive effects, while others may have negative effects. -/- In this context, in this letter, we wanted to emphasize the need to (...) have an idea about the impact on academic processes and the many changes we face and are likely to face in the future. -/- The first, perhaps the best known, is open-access publishing and preprint servers. In the literature, the impact of open-access publishing and preprint servers has long been debated. The general consensus is that both situations have positive effects.2,3 In contrast, predatory journals and their negative effects, which are associated with the misuse of the open-access publishing system and that exploit the author-pay model, are another important topic of discussion.4,5 Another situation that may be encountered is clone journals. These are fake copies of real journals that deceive authors with identical names, logos, and designs.5 Thus, technological developments allow for positive effects, such as open-access publishing and preprint servers, but also negative effects, such as predatory or clone journals. -/- Another issue is the impact of challenging conditions on academic publications. Major global events can often have a dramatic impact on science.6 Undoubtedly, the most prominent example of this is the COVID-19 pandemic. This pandemic has severely affected academic productivity and the content of articles. Although the quarantine processes slowed down the research and experimentation phases of scientific studies, academic adaptation began to be achieved with the use of online communication platforms, which increased in effectiveness around the same time.6 -/- In the same time period, it is known that there is an increasing trend in studies that do not require ethics committee approval and do not require experimentation, such as bibliometric7, altmetric analyses8, assessing the quality and reliability of the YouTube videos9,10, etc., where web-based information analysis is performed. Alternative solutions are, therefore, possible even in challenging conditions on a scale that will affect the world. -/- Another important example is artificial intelligence tools, one of the most prestigious topics of recent times. As in every field, artificial intelligence tools have had very important effects in the academic field, especially in the last year, and it is thought that these effects will continue for a long time. Thanks to these tools, it is possible to produce academic publications in a shorter time and with higher quality. In contrast, the use of artificial intelligence tools has also raised ethical issues such as data security, patient privacy, scientific accuracy, scientific responsibility, or the acceptance of false information as truth.11,12 -/- In conclusion, it is possible to encounter different and even unique situations in every period of academic productivity. In addition, these situations can have both positive and negative aspects. One of the most important tricks for an ideal academic is to understand the situation well, adapt quickly, take advantage of the positives, and avoid the negatives. (shrink)

Cloud computing has revolutionized data management for businesses and individuals a like, ushering in an era of unprecedented accessibility and scalability. As demand for cloud services continues to surge, the imperative for efficient and secure systems becomes paramount. One approach to meeting this challenge is the consolidation of virtual machines onto fewer physical servers, optimizing resource utilization and yielding significant energy savings. Moreover, this consolidation strategy bolsters overall security by enabling more effective monitoring and control of virtual machine instances. Through (...) the implementation of encryption, access control mechanisms, and intrusion detection systems, sensitive data can be safeguarded against unauthorized access, thereby mitigating potential risks and vulnerabilities inherent in cloud computing environments. In the realm of cloud computing, security and efficiency are paramount concerns, driving innovation and evolution in system design and management. By consolidating virtual machines onto fewer physical servers, organizations can realize tangible benefits in terms of resource optimization and energy efficiency. Yet, beyond the operational advantages, this consolidation approach also enhances the security posture of cloud environments. Through the adoption of robust security measures such as encryption and intrusion detection, sensitive data can be shielded from unauthorized access, bolstering the integrity and trustworthiness of cloudbased systems. (shrink)

The goal of this project is to create a prototype smart farm using Internet of Things (IoT) technology. Developing an Internet of Things feeding control system and designing and implementing environmental monitoring and control systems for farm housing are the primary objectives. The development method uses open-source hardware and software and is conducted in pig farms in the Thai region of Nakhon Si Thammara. An ESP8266 Wi-Fi microcontroller is crucial to the system design, allowing several sensors to be connected to (...) the server and actuators to be controlled. The MQTT protocol is used to transmit data over a secure wireless local network. The data flow is designed using Node-RED, and storage is handled using a NoSQL database such as InFluxDB. The feeding control system achieved a 100% compliance rate by following instructions continuously. End customers can utilise a web application on their PCs or cellphones to obtain real-time data and operate the system. Furthermore, the collected data is processed and analysed as big data, which facilitates the analysis of climate change trends in the vicinity of the study site and forecasting. The overall performance rating of the system received great marks from users, averaging 4.40 out of 5 with a 0.46 standard deviation. As a result, the created IoT system functions as a prototype that may be further enhanced for use in bigger farms or other agricultural environments. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. These (...) previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

Edge computing, combined with machine learning (ML), is emerging as a transformative paradigm for handling the data deluge generated by the Internet of Things (IoT) devices. Traditional cloud computing is often inadequate for the low-latency, high-throughput demands of IoT applications, especially in real-time analytics. By processing data locally at the edge of the network, edge computing reduces latency, enhances privacy, and alleviates the bandwidth burden on centralized cloud servers. The integration of ML algorithms into edge devices further augments the decision-making (...) process by enabling real-time data analytics, improving operational efficiency, and delivering immediate insights. This paper explores the synergy between edge computing, ML, and IoT, discussing how real-time data analytics at the edge can transform industries such as smart cities, healthcare, and industrial automation. We also present challenges, future directions, and practical applications of machine learning-based real-time analytics for edge computing in IoT ecosystems. (shrink)

Road traffic accidents are one of the leading causes of death and severe injury globally, primarily due to delayed emergency responses. The time immediately following a traumatic injury, known as the "golden hour," is critical for administering medical aid, significantly enhancing survival rates. To address the urgent need for rapid response mechanisms, this paper presents an advanced Internet of Things (IoT)-based Automotive Accident Detection and Intimation (ADI) system. The proposed system leverages a combination of embedded and connected smartphone sensors to (...) detect vehicular collisions and assess accident severity with high precision. A microcontroller unit interfaces with various sensors—such as accelerometers, gyroscopes, and environmental monitors—as well as GPS technology to track vehicle movement and conditions. Upon detecting a probable accident, the system autonomously triggers an alert mechanism that transmits real-time data, including GPS coordinates and sensor information, to a centralized server. This information is promptly relayed to emergency response units such as ambulances, police stations, hospitals, and fire departments, along with notifying a pre- registered emergency contact. The system architecture ensures minimal human intervention, thereby reducing response time and increasing reliability. Additionally, the platform includes a remote server interface that enables real-time monitoring and analysis of incident data. By enhancing accident detection accuracy and reducing the latency in emergency communication, the ADI system has the potential to save lives and minimize post-accident trauma. This research contributes to the advancement of intelligent transportation systems and paves the way for safer, smarter vehicular technologies. (shrink)

At present, numerous innovations and technologies take place continuously. Serverless computing is a paradigm switch from conventional cloud computing. The term ‘serverless’ represents that the cloud developers do not interact with servers directly, whereas they use codes and logic to do so. This feature enhanced and extended the overall performance of the serverless platform. This paper presents a comparative study of both cloud environments from conventional to platform-independent or serverless computing. Apart from this, several key aspects such as definitions benefits, (...) limitations, challenges, real-world applications, and futuristic direction are discussed in a well-organized form. (shrink)

This paper extensively develops and thoroughly creates a highly centralized and extremely efficient log collection system utilizing and leveraging the robust syslog server and comprehensively collects and gathers log data from the numerous hosts residing within the network, meticulously analyses and scrutinizes the detailed log files and promptly sends and transmits a critical alert message to the central host through secure and reliable SMS. This paper thoroughly and comprehensively addresses and tackles the increasingly growing and rapidly expanding need for (...) extremely robust and highly reliable cybersecurity measures and solutions in the vulnerable and sensitive power sector. (shrink)

With the rising global prevalence of cardiovascular diseases and the growing need for accessible healthcare solutions, there is a critical demand for systems that can provide both continuous monitoring and early diagnosis. This project presents a Smart Health Monitoring System with Real-Time Vital Tracking and Predictive Diagnostics, a comprehensive and intelligent health management platform that combines Internet of Things (IoT) technology with machine learning- based predictive analytics. The system integrates a heartbeat sensor and DS18B20 temperature sensor connected to an ESP32 (...) microcontroller, which continuously captures vital physiological parameters. This data is transmitted wirelessly to a Flask-powered web server, where it is logged, visualized in real time through an interactive dashboard, and stored for further analysis. Users can monitor trends over time, download data in CSV format, and reset logs as needed. (shrink)

Each wagon is equipped with sensors that continuously capture and transmit weight data to the central server via Node MCUs, enabling real-time analysis through the Blynk app. By monitoring the load conditions, the system ensures wagons remain within safe weight limits, preventing overloading, which can cause damage and safety hazards, or under-loading, which reduces operational efficiency. A key feature of the system is its use of machine learning algorithms to detect patterns and anomalies related to load distribution. When potential (...) risks are identified, the system generates automated alerts, prompting interventions such as load adjustments, wagon rerouting, or even halting operations when necessary. (shrink)

This paper addresses the always online behavior of the data owner in proxy re- encryption schemes for re-encryption keys issuing. We extend and adapt multi-authority ciphertext policy attribute based encryption techniques to type-based proxy re-encryption to build our solution. As a result, user authentication and user authorization are moved to the cloud server which does not require further interaction with the data owner, data owner and data users identities are hidden from the cloud server, and re-encryption keys are (...) only issued to legitimate users. An in depth analysis shows that our scheme is secure, flexible and efficient for mobile cloud computing. (shrink)

Despite the extensive research conducted in the field of Cloud Computing, challenges still persist regarding workload balancing in cloud-based applications, particularly within the Infrastructure as a Service (IaaS) model. Efficient task allocation is vital in cloud computing due to the limited availability of resources and virtual machines. IaaS is a model that manages the backend infrastructure, including servers, data centres, and virtual machines. Cloud Service Providers must ensure optimal service delivery performance in these models to prevent issues like host overload (...) or underload, which can lead to increased execution times or machine failures. Task Scheduling plays a significant role in load balancing, and it is essential that task scheduling aligns with the requirements outlined in the Service Level Agreement (SLA), a document provided by cloud developers to users. Key SLA parameters, such as deadlines, are considered in the load balancing algorithm. The proposed algorithm aims to optimize resource usage and enhance load balancing by taking into account Quality of Service (QoS) task parameters, VM priorities, and resource allocation. This load balancing algorithm addresses the identified challenges and fills the current research gap based on findings from the literature. Results indicate that the proposed algorithm achieves an average resource utilization of 90% compared to the existing Dynamic LBA algorithm, while also demonstrating improved performance in terms of reduced execution time and makespan. (shrink)

Pada percobaan kedua ini, kami memperbarui data untuk chatbot yang disediakan di [1] dengan mengunggah artikel baru [2] ke server Chatbase untuk pembelajaran. Kami masih ingin mengeksplorasi keterampilan pemahaman bacaan Chatbase serta kemampuannya dalam menyampaikan konten yang diringkas atau diekstraksi secara khusus dari artikel yang disediakan [2]. Selain itu, dalam perintah yang kami berikan kepada Chatbase mengenai konten [2], kami mencoba mempelajari apakah kecerdasan buatan dapat menghubungkan konten yang dibaca dengan prinsip-prinsip yang tecantum di [3-4]. Kami menyediakan percakapan rinci (...) dengan Chatbase sebagai berikut. Perintah 1: Apa pembelajaran utama dari artikel ini? Chatbase: Artikel ini membahas pentingnya retraksi dalam penelitian ilmiah dan menyarankan cara untuk meningkatkan proses retraksi. Penulis berpendapat bahwa retraksi pada dasarnya tidak buruk, melainkan merupakan cara praktis untuk memperbaiki kesalahan dan memperkuat upaya ilmiah. Artikel tersebut menyarankan bahwa mewajibkan pengungkapan kekurangan dapat mencegah pernyataan yang berlebihan dalam sebuah penelitian dan bahwa menguraikan empat jenis informasi jika ada retraksi informasi dapat mendorong peneliti dan institusi untuk mendukung retraksi jika diperlukan. Artikel tersebut juga menyebutkan ceklis integritas publikasi yang dapat membantu menunjukkan masalah atau kekeliruan tanpa memerlukan pernyataan resmi atas pelanggaran atau kesalahan yang dilakukan. (shrink)