Beach pollution is an escalating global issue, posing a significant threat to marine ecosystems and human health. Plastic waste, originating from commercial activities, fishing, and improper waste management, is preva...
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ISBN:
(数字)9798350354645
ISBN:
(纸本)9798350354652
Beach pollution is an escalating global issue, posing a significant threat to marine ecosystems and human health. Plastic waste, originating from commercial activities, fishing, and improper waste management, is prevalent on beaches worldwide. This debris is transported by rivers and waterways, leading to widespread contamination. Additionally, climate change and pollution are expected to result in the loss of many sandy beaches, altering ecosystems and diminishing natural recreational areas. For this reason, an IoT-based system has been developed that, using low-cost autonomous cleaning robots on different beaches, can collect data and detect garbage using the new YOLO V10 architecture. As a result, the system was successfully implemented to store and alert users when a beach has a high level of pollution. It efficiently collects and saves data from multiple beaches in a safe nosql Database using multiple robots simultaneously. Additionally, the highest performance predictive model achieved an accuracy of 77.69%, a recall of 71.63%, and an F1 score of 74.53%.
The advent of cryptocurrencies has paved the way for an alternative financial system based on blockchain technology. The properties of this technology allow untrustworthy entities to exchange transactions with each ot...
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ISBN:
(数字)9798350362480
ISBN:
(纸本)9798350362497
The advent of cryptocurrencies has paved the way for an alternative financial system based on blockchain technology. The properties of this technology allow untrustworthy entities to exchange transactions with each other, confidentially, transparently and anonymously. This latter property combined to the solutions for mixing cryptocurrency transactions has contributed significantly to strengthening the anonymization of cryptocurrencies. Consequently, they have facilitated illegal activities. Nowadays, the majority of financial crimes are carried out using cryptocurrencies. To mitigate this problem, cryptocurrencies forensics techniques have been suggested with the aim of reducing illicit financial activities. However, most of these existing approaches do not support real-time processing, do not offer architecture for Big Data processing. Moreover, these methods do not provide an optimized data storage system based on a graph database. This paper performs a comprehensive investigation into cryptocurrencies forensics techniques including real-time big data processing and graph database storage. It explores and classifies existing cryptocurrencies forensics techniques to effectively support decision making for their implementation. Furthermore, it reviews recent research effort to enforce graph data storage system, big data and real-time processing while executing cryptocurrencies forensics. Through this paper, we aim to clear up the outlook by suggesting an outline of the different method, tools and application when raising the basic differences and presenting associated advantages and limitations. More importantly, we underline current challenges and future research directions to stimulate research in cryptocurrencies forensics with real-time and big data processing using graph data storage.
nosql database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of nosql database such as MongoDB proves to improve development lifecycles ...
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ISBN:
(数字)9798331542313
ISBN:
(纸本)9798331542320
nosql database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of nosql database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of nosql database, and nosql database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of nosql database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model.
International freight transport is a growing market and a crucial component of the global economy, enabling the seamless flow of goods across regions and supporting global trade. However, the industry faces significan...
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ISBN:
(数字)9798331502461
ISBN:
(纸本)9798331502478
International freight transport is a growing market and a crucial component of the global economy, enabling the seamless flow of goods across regions and supporting global trade. However, the industry faces significant challenges, particularly in road transport orchestration, due to the increasing complexity of operations and the demand for real-time decision-making. This study sketched the algorithmic and programming framework to address these challenges by presenting an optimized database architecture designed to improve decision support systems for freight transport. The proposed system architecture may leverage the flexibility and scalability of nosql databases to manage large, semi-structured datasets in the future, while REST APIs enable seamless integration and real-time data manipulation. To enhance usability and transparency, the system incorporates advanced data visualization tools, allowing stakeholders to derive actionable insights and improve operational efficiency. By focusing on areas such as real-time tracking, sustainability, and process optimization, this work not only addresses current operational needs but also provides a robust foundation for future developments, including advanced analytics and mobile application integration. This contribution supports the logistics industry's shift toward more adaptive, efficient, and sustainable transport solutions.
Internet of Things (IoT) devices generate large amounts of data, creating the challenge of designing efficient IoT cloud storage solutions. This study focuses on an IoT application managing air quality measurement dat...
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ISBN:
(数字)9798331533366
ISBN:
(纸本)9798331533373
Internet of Things (IoT) devices generate large amounts of data, creating the challenge of designing efficient IoT cloud storage solutions. This study focuses on an IoT application managing air quality measurement data, which requires frequent retrieval of recent data for near-real-time monitoring and access to long segments of time-series data. The study performs a comparative analysis of three IoT storage system architecture options on Amazon Web Services (AWS) cloud. In contrast to existing literature, the options take into account data access and retrieval patterns specific to the use cases of the IoT application under study, where only data produced by a particular device is accessed at a time. The first option utilizes a nosql database, the second employs data streams and an object storage, and the third uses a time-series database. Each option was tested by performing retrievals of different lengths of time segments, focusing on retrieval times and query capabilities. Results show that a time-series database is suitable for applications benefiting from query options or time-series functions, such as interpolation. A Key-Value nosql database is suitable for applications retrieving short time segments without extensive queries. Conversely, an object storage excels in storing and retrieving large volumes of raw data, while data streams are unsuitable as a direct source of data for near-real-time applications. The study provides a framework for comparing alternative architectures to analyze their suitability, guiding decisions in selecting the most appropriate storage solution based on specific IoT application requirements.
nosql injection is a security vulnerability that allows attackers to interfere with an application’s queries to a nosql database. Such attacks can result in bypassing authentication mechanisms, extracting or altering...
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ISBN:
(数字)9798331522100
ISBN:
(纸本)9798331522117
nosql injection is a security vulnerability that allows attackers to interfere with an application’s queries to a nosql database. Such attacks can result in bypassing authentication mechanisms, extracting or altering data, causing denial of service, or even executing code on the server. Unlike traditional SQL databases, nosql databases use varied query languages and data structures, which results in different types of injection vulnerabilities. The two primary forms of nosql injection are syntax injection, where attackers manipulate query syntax similar to SQL injection techniques, and operator injection, where attackers leverage query operators to manipulate queries. This paper focuses on a practical example of operator injection and outlines effective prevention methods, including input sanitization, parameterized queries, access control, encryption, network security, and compliance with security standards. These strategies aim to mitigate the risks associated with nosql injection and enhance the overall security of applications using nosql databases.
This paper presents the design and implementation of a machine learning-driven web system for automating the transformation of sequential code into parallel code. The proposed system leverages a shared memory programm...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
This paper presents the design and implementation of a machine learning-driven web system for automating the transformation of sequential code into parallel code. The proposed system leverages a shared memory programming model and is built on a microservices architecture to ensure scalability and modularity. It incorporates advanced tools for constructing and analyzing Abstract Syntax Trees to generate parallel programs that adhere to Bernstein's conditions. A supervised learning mechanism, using Word2Vec for vector representation and TF-IDF for significance evaluation, enables the system to adapt and refine parallelization rules based on programmer input. Additionally, the system integrates an AMQP-based message broker to handle high-load processing efficiently and supports data storage through relational or nosql databases. The solution provides a robust and flexible platform for enhancing computational performance and reducing development complexity in parallel programming. The results demonstrate the feasibility and effectiveness of this approach in addressing the challenges of automating code parallelization in modern software systems.
Efficiently and accurately retrieving specific information from healthcare datasets, such as the Vaccine Adverse Event Reporting System (VAERS) 1 , presents significant challenges. A promising solution to this proble...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Efficiently and accurately retrieving specific information from healthcare datasets, such as the Vaccine Adverse Event Reporting System (VAERS)
1
, presents significant challenges. A promising solution to this problem is the Text-to-ESQ approach, which is akin to Text-to-SQL tasks but leverages nosql database Elasticsearch, to thoroughly explore VAERS data. Non-relational databases are particularly adept at managing complex and dynamic data formats, thereby enabling the extraction of more valuable insights. However, generating executable nosql queries is still challenging due to the limited availability of nosql query datasets, which constrains model training. One potential remedy involves the use of large language models (LLMs), which can be applied in few-shot and even zero-shot learning scenarios. Nonetheless, the lack of prior evaluation for this novel task, coupled with the absence of a comprehensive, unbiased assessment of existing LLMs and prompting strategies, impedes the development of a robust architecture. Motivated by these challenges, we introduce a new Instruction-Enhanced Explainable (InstructEx) Chain-of-Thought (CoT) prompting by integrating existing CoT prompts and conducting a comprehensive investigation of LLMs and CoT prompting. The extensive experimental analysis demonstrates the effectiveness of using LLMs for Text-to-ESQ when combined with the InstructExCoT prompting. It also sheds light on the strengths and weaknesses of these methods from multiple perspectives.
In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively addre...
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ISBN:
(数字)9798350386059
ISBN:
(纸本)9798350386066
In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively address the multifaceted security challenges within blockchain networks, particularly focusing on Ethereum contracts. Central to the platform is a dual-database architecture, blending a nosql database with a graph database, enhancing data management, and enabling intricate transaction network visualizations. The platform's Threat Detection module, utilizing Large Language Models (LLMs) in conjunction with traditional methods, offers a novel approach to identifying and categorizing vulnerabilities in Ethereum smart contracts. Complementing this, the Threat Evidence Collection module provides detailed post-attack analysis, tracing transactions to their sources and evaluating address risks. This module's capabilities extend to producing statistical reports, including the transactional history and risk evaluation of individual addresses. Demonstrated on the Ethereum blockchain, the platform showcases its proficiency in handling complex data, rapid threat detection, and extensive forensic analysis, presenting a robust solution to fortifying blockchain security and offering a proactive defense mechanism for users and developers in the blockchain environment.
This paper analyzes the demand of characteristic database construction, and expounds the construction of characteristic database system with nosql, so as to meet the application demand of characteristic database in co...
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ISBN:
(数字)9798350393682
ISBN:
(纸本)9798350393699
This paper analyzes the demand of characteristic database construction, and expounds the construction of characteristic database system with nosql, so as to meet the application demand of characteristic database in colleges and universities.
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