Health care is crucial for living a pleasant life. However, getting a doctor's appointment for a checkup is exceedingly challenging. Before contacting a doctor, it is suggested that healthcare chatbots be develope...
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In numerous countries, over 50% of the workforce is engaged in the informal sector, lacking social protection for healthcare and facing a lack of regulatory enforcement for occupational health and safety standards. Th...
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MLOps (machinelearning Operations) is an engineering approach to streamline the development, deployment and maintenance of machinelearning (ML) solutions in an operational environment. Managing the ML life-cycle at ...
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ISBN:
(纸本)9798350363029;9798350363012
MLOps (machinelearning Operations) is an engineering approach to streamline the development, deployment and maintenance of machinelearning (ML) solutions in an operational environment. Managing the ML life-cycle at scale poses a variety of challenges which MLOps addresses, from the inter-dependency of various systems and their interoperability to the deployment of scalable pipelines. The maritime industry is no exception to this. This sector encounters distinct challenges in implementing machinelearning operations, such as predicting the weather, optimizing shipping routes, and detecting anomalies in vessel behaviour. These requirements are addressed by creating specialized ML models tailored to the maritime domain. However, developing and deploying these models can be challenging due to the complexity of the maritime environment and the need for real-time decision-making. This study uses a systematic mapping analysis to evaluate and index existing literature on frameworks and practices for MLOps solutions that would be suitable for maritime applications. The discussion section addresses recommendations for applying MLOps to the maritime domain, difficulties with implementation and possible solutions, security, privacy, and already-implemented use cases, as well as future perspectives.
Phishing websites have become a significant cybersecurity threat, hosting malware and exploiting users by mimicking popular sites. Victims suffer financial loss, compromised privacy, and damaged reputation. Urgent sol...
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The prevalent issue of increased student dropouts, shared by universities worldwide, often culminates in decreased academic performance and prolonged completion times for degree programs. Prompt detection of those stu...
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ISBN:
(纸本)9783031630309;9783031630316
The prevalent issue of increased student dropouts, shared by universities worldwide, often culminates in decreased academic performance and prolonged completion times for degree programs. Prompt detection of those students facing a likely chance of failing a course could allow universities to intervene with sufficient support and guidance, facilitating an improvement in their performances. Numerous studies have explored the problem of performance prediction from various perspectives using different representations, algorithms, and data sets. The diversity in research strategies, however, complicates comparisons. In this study, we present a thorough evaluation of various predictive algorithms, representations, and predictive targets for the task of predicting student performance across 77 different courses in three distinct programs at the Universidad de los Andes: systems and Computer engineering, Industrial engineering, and Economics. The results show that representing data in windows of time spanning 3 previous semesters, in conjunction with the LSTM-based algorithm for binary classification, yields the best results, achieving a precision of 0.838.
In the current landscape of healthcare, machinelearning (ML) and the Internet of Things (IoT) are making significant strides. Ongoing research focuses on remote patient monitoring, disease prediction, and diagnostic ...
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This study reviews the potential benefits of machinelearning techniques in diagnosing ocular disorders, aiming to address the limitations of traditional diagnostic methods. Various machinelearning (ML) algorithms, i...
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In medical field, heart diseases are painstaking as a life-threatening disease. Diagnosing and predicting the outlook of cardiovascular diseases are pivotal tasks in medicine, aiding cardiologists in accurately catego...
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This paper explores prediction and forecasting methods, focusing on the concept of superforecasting and machinelearning in event forecasting. The study emphasizes the role of accounting systems in business decision-m...
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heart disease is a leading global cause of death, and predicting it is complex, requiring advanced expertise beyond doctors' ease. The medical environment remains "Information rich"and "Information ...
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