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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Diabetes is a serious health concern almost all over the countries as it impaired many organs in human body and rate of affected person is gradually increasing. Several significant factors have been incorporated for p...
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作者：
Wanjari, KetanVerma, Prateek
Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India
Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have si...
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Embedding domain expertise within machinelearning has allowed for hybrid models that possess both the expressivity of data-driven models whilst adhering to fundamental equations and constraints that ensure prediction...
The proceedings contain 200 papers. The topics discussed include: automated brain tumor detection and classification through deep learning analysis of MRI scans;Tri-UnityNet: a multifaceted ensemble model for pneumoni...
ISBN:
(纸本)9798350364828
The proceedings contain 200 papers. The topics discussed include: automated brain tumor detection and classification through deep learning analysis of MRI scans;Tri-UnityNet: a multifaceted ensemble model for pneumonia detection;breast cancer detection using neural networks;electric vehicle battery health monitoring system;early detection of cardiovascular disorders using enhanced ANN model;Healthbot analytics: optimizing healthcare efficiency through intelligent integration;driver drowsiness detection using Mobilenetv2 with transfer learning approach;identification of uterine cervical cancer using CNN compared to ANFIS Approach On MRI Images;violence detection through surveillance videos using combination of VGG16 And LSTM;and OLFV: harnessing the power of enhanced deep learning model to recognize fingerprints using optimization and classification principles.
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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作者：
Petkar, Taniya
Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India
This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughou...
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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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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.
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