Despite being proposed as early as 1959, COBOL (Common Business-Oriented Language) still predominantly acts as an integral part of the majority of operations of several financial, banking, and governmental organizatio...
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The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Cons...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Consequently, mobile payment systems centered on electronic wallets (e-wallets) have signifi-cantly transformed contemporary finance, introducing improved security measures and transaction functionalities. However, the prevalence of unlawful activities, including money laundering and associated fraud via e-wallets, presents a substantial threat to the integrity of the financial sector. This research paper delves into the pivotal role of machine learning models in identifying money laundering activities within e-wallet transactions. The study focuses on addressing the imbalance inherent in the PaySim dataset through the oversampling technique. Employing three distinct models—Logistic Regression (LR), Gradient Boosting, and XGBoost—the research systematically evaluates their effectiveness. Notably, XGBoost emerged as the standout performer, showcasing exceptional accuracy at 99.88%, precision at 0.9984, and sensitivity at 0.999. Furthermore, a threshold moving technique is employed to enhance the model’s efficiency, optimizing its performance in detecting potential instances of money laundering within e-wallet transactions.
Elements of randomness are a very common factor in modern digital games, from simple rolls of a die to complex AI systems. These elements have an impact on how the player experiences a game. We believe that exploring ...
Elements of randomness are a very common factor in modern digital games, from simple rolls of a die to complex AI systems. These elements have an impact on how the player experiences a game. We believe that exploring the field of luck analysis can benefit designers through a developed understanding of how such elements affect players. In this study, we explore how elements of randomness affect players in the roguelike deckbuilding game, Slay the Spire using data clustering. Three player skill groups were identified with the use of clustering: Winners, Low skill losers and High skill losers. Our results indicate that people who succeeded in beating the game, had an increased amount of randomness in the form of cards by a factor of 1.82. Showing that more skilled players do not shy away from randomness but instead embrace it more than lower skilled players.
This paper propose a framework Knowledge advantage machine (KAM) to help in organizing individually discovered knowledge drawn from a narrowly bounded domain into a personal knowledge network based on personal request...
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This paper propose a framework Knowledge advantage machine (KAM) to help in organizing individually discovered knowledge drawn from a narrowly bounded domain into a personal knowledge network based on personal request and tags. Ontologies, folksonomy and personomy are employed in KAM to constitute the useful repositories of knowledge. Ontologies offer a flexible and expressive layer of abstraction, very useful for capturing the semantics of information repositories, but they can not reflect the user's interest. The user in KAM can freely choose the words to tag the resources which are the reflection of the user's own interest. The set of tags and tagged knowledge of a user comprise the personomy. In a group the shared tags and knowledge are known as folksonomy. Our approach investigates how to map these tags in personomy and folksonomy to existing domain ontology in order to add accurate meanings. The user's behaviors are also used to re-rank the query results. So the user can find the useful knowledge quickly and accurately.
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of ...
ISBN:
(纸本)9798331314385
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs.
This paper analyzes the current status,technological developments,academic exchange platforms,and future challenges and solutions in the field of intelligent ophthalmology(IO)in *** terms of technology,significant pro...
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This paper analyzes the current status,technological developments,academic exchange platforms,and future challenges and solutions in the field of intelligent ophthalmology(IO)in *** terms of technology,significant progress has been made in various areas,including diabetic retinopathy,fundus image analysis,quality assessment of medical artificial intelligence products,clinical research methods,technical evaluation,and industry *** continually enhance the safety and standardization of IO technology by formulating a series of clinical application guidelines and *** establishment of domestic and international academic exchange platforms provides extensive collaboration opportunities for professionals in various fields,and various academic journals serve as publication platforms for IO ***,challenges such as technological innovation,data privacy and security,lagging regulations,and talent shortages still pose obstacles to future *** address these issues,future efforts should focus on strengthening technological research and development,regulatory framework construction,talent cultivation,and increasing patient awareness and acceptance of new *** comprehensively addressing these challenges,IO in China is poised to further lead the industry’s development on a global scale,bringing more innovation and convenience to the field of ophthalmic healthcare.
Measuring the stellar parameters of A-type stars is more difficult than FGK stars because of the sparse features in their spectra and the degeneracy between effective temperature(T_(eff))and gravity(log g).Modeling th...
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Measuring the stellar parameters of A-type stars is more difficult than FGK stars because of the sparse features in their spectra and the degeneracy between effective temperature(T_(eff))and gravity(log g).Modeling the relationship between fundamental stellar parameters and features through machine learning is possible because we can employ the advantage of big data rather than sparse known *** soon as the model is successfully trained,it can be an efficient approach for predicting Teffand log g for A-type stars especially when there is large uncertainty in the continuum caused by flux calibration or *** this paper,A-type stars are selected from LAMOST DR7 with a signal-to-noise ratio greater than 50 and the Teffranging within 7000 to 10,000 *** perform the Random Forest(RF)algorithm,one of the most widely used machine learning algorithms to establish the regression relationship between the flux of all wavelengths and their corresponding stellar parameters(T_(eff))and(log g)*** trained RF model not only can regress the stellar parameters but also can obtain the rank of the wavelength based on their sensibility to *** to the rankings,we define line indices by merging adjacent *** objectively defined line indices in this work are amendments to Lick indices including some weak *** use the Support Vector Regression algorithm based on our new defined line indices to measure the temperature and gravity and use some common stars from Simbad to evaluate our *** addition,the Gaia Hertzsprung-Russell diagram is used for checking the accuracy of Teffand log g.
The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals ...
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In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, ...
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ISBN:
(数字)9798331528126
ISBN:
(纸本)9798331528133
In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, often encounter limitations such as data sparsity, cold start problems, and scalability issues. This paper introduces a novel approach that integrates MapReduce technology with Genetic Algorithms (GAs) to address these chal-lenges. Utilizing the Hadoop framework, our MapReduce Genetic Algorithm (MRGA) efficiently processes extensive datasets by distributing tasks across a cluster of machines. The genetic algorithm component optimizes recommendation accuracy through advanced techniques like selection, crossover, and mutation. Our experimental results, based on the MovieLens 100K dataset, demonstrate that the MRGA approach outperforms traditional collaborative filtering methods in terms of recommendation accuracy and scalability. By leveraging MapReduce's distributed computing power and the GA's optimization capabilities, this research offers a robust solution to improve movie recommendations and handle large-scale data efficiently.
The technology driven of pineapple size and maturity grading is a useful marketing strategy to enhance proper utilization and increase of profits. This comprehensive paper introduces an advanced methodology for pineap...
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