The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volum...
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Continual learning in machinelearningsystems requires models to adapt and evolve based on new data and experiences. However, this dynamic nature also introduces a vulnerability to data poisoning attacks, wheremalici...
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Continual learning in machinelearningsystems requires models to adapt and evolve based on new data and experiences. However, this dynamic nature also introduces a vulnerability to data poisoning attacks, wheremaliciously crafted input can lead to misleading model updates. In this research, we propose a novel approach utilizing theEdDSAencryption system to safeguard the integrity of data streams in continual learning scenarios. By leveraging EdDSA, we establish a robust defense against data poisoning attempts, maintaining the model's trustworthiness and performance over time. Through extensive experimentation on diverse datasets and continual learning scenarios, we demonstrate the efficacy of our proposed approach. The results indicate a significant reduction in susceptibility to data poisoning attacks, even in the presence of sophisticated adversaries.
Cardiovascular disorders represent a general and critical health concern worldwide, which needs an innovative approaches to increase prediction and prevention strategies. This research paper introduces a cutting-edge ...
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Cancer research relies on accurate classification models. Detecting cancer early enhances the chance of a cure. This paper proposes an innovative classification model using enhanced ML (machinelearning) algorithms fo...
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作者：
Anbalagan, E.Sasikumar, S.Kumar, M. Guru VimalParamesh, J.Sriram, K.P.Professor
Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor
Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor
Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor
Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor
Information Technology St.Joseph's Institute of Technology Chennai India
By introducing this collaborative filtering algorithm, which is dependent on machinelearning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personal...
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In IoT, sensor technology is the link between the physical and digital worlds, generating huge amounts of real-time data. machinelearning empowers IoT systems to sense intelligently by processing this data. Sensors c...
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ISBN:
(纸本)9798350375084;9798350375077
In IoT, sensor technology is the link between the physical and digital worlds, generating huge amounts of real-time data. machinelearning empowers IoT systems to sense intelligently by processing this data. Sensors can not only sense environmental parameters such as temperature and humidity, but also obtain information on equipment operation status and productivity. And machinelearning can learn the laws from these data to achieve real-time monitoring and anomaly detection. Through machinelearning algorithms, the system is able to recognise equipment anomalies, predict faults and take action before an incident occurs. This not only improves the reliability and stability of equipment, but also reduces maintenance costs. The combination of intelligent sensing and data analytics makes IoT systems smarter and more efficient, bringing significant improvements to applications in a variety of fields. The system is able to extract valuable information from data, identify anomalies and trends, and help users better understand environmental conditions. For example, in a smart home, the system learns the living habits and preferences of family members and adjusts the operating status of home devices to provide a more comfortable and intelligent living experience.
Within the dynamic field of planetary defence, machinelearning has emerged as a key component that is essential to early warning systems that are tasked with forecasting the orbits and trajectories of potentially dan...
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A strategic tactics is required to navigate the challenging process of athlete selection within budgetary limits, seeing it as a multiple-objective, multi-criteria optimization challenge. However, judgments made by hu...
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The education sector is poised for a significant shift propelled by Artificial Intelligence (AI) technologies. This paper explores the various uses of AI in education, such as data-driven decision assistance for instr...
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The field of plant care has seen significant advancements with the increasing application of artificial intelligence (AI) and machinelearning (ML) techniques across various domains. This review explores the integrati...
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