In this paper, a multivariate linear regression model is built for prediction based on SBPE dataset by drawing heat maps to select relevant features. All 80% of the data is used as a training set. The remaining 20% wa...
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A novel low-cost, surface relief mapping capable mobile robot for the purpose of space exploration and mapping is developed and presented. With the help of machinelearning post-processing of obtained data in IMU (Ine...
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In this paper, we use contrastive learning to address anomalous sound detection under domain shifts for the slider industrial machine. Contrastive learning is an innovative approach that uses augmentation techniques t...
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ISBN:
(纸本)9798350372977;9798350372984
In this paper, we use contrastive learning to address anomalous sound detection under domain shifts for the slider industrial machine. Contrastive learning is an innovative approach that uses augmentation techniques to learn the invariant attributes of samples. Our work involves assessing the effect of each of several augmentation methods on the slider machine and fine-tuning the parameters of the most effective method. We consider domain-shifted data to better simulate real-world cases, which tend to have characteristics that differ from those seen in the training data. Based on our experiments, we propose the preferred augmentation technique and parameters for slider domain-shifted data. The results show that using slider-specific techniques and parameters improves performance on slider domain-shifted data by 3.0% when compared to using more generic techniques and parameters.
Obsessive Compulsive Disorder is a serious psychological health condition featured by continuous thoughts and monotonous episodes, accordingly, impacting one's daily life condition. With Early plus accurate predic...
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Skin is our body's most important organ;skin disorders are currently a common and serious problem due to patients' sensitive skin, as well as physical and psychological consequences. Skin disease symptoms migh...
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Detecting mental health problems at the earliest can help professionals provide correct treatment and increase the value of patients. One of the most prevalent types of impairment in the world is depression. It is imp...
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Context: The rise of Artificial Intelligence (AI) and machinelearning (ML) applied in many software-intensive products and services introduces new opportunities but also new security challenges. Motivation: AI and ML...
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ISBN:
(纸本)9798400705915
Context: The rise of Artificial Intelligence (AI) and machinelearning (ML) applied in many software-intensive products and services introduces new opportunities but also new security challenges. Motivation: AI and ML will gain even more attention from industry in the future, but threats caused by already discovered attacks specifically targeting ML models are either overseen, ignored, or mishandled. Problem Statement: Current Software engineering security practices and tools are insufficient to detect and mitigate ML Threats systematically. Contribution: We will develop and evaluate a threat modeling technique for non-security experts assessing ML-intensive systems in close collaboration with industry and academia.
The proliferation of smart contracts has led to a surge in hacking attacks, resulting in substantial financial losses and undermining the healthy growth of the blockchain ecosystem. To mitigate these challenges, this ...
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The meteorological data corresponding to Vellore Institute of Technology, Chennai is downloaded by pinpointing the institute's latitude and longitude in the National Solar Radiation data Base viewer. The photovolt...
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In the contemporary landscape of cyber threats, Botnet attacks emerge as a pervasive and evolving menace, demanding sophisticated countermeasures. This paper presents a comprehensive development of an Intrusion Detect...
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ISBN:
(纸本)9798350372977;9798350372984
In the contemporary landscape of cyber threats, Botnet attacks emerge as a pervasive and evolving menace, demanding sophisticated countermeasures. This paper presents a comprehensive development of an Intrusion Detection System (IDS) utilizing advanced machinelearning techniques to thwart Botnet intrusions. Central to this IDS is an ensemble voting classifier, a synergistic integration of multiple algorithms, tailored to augment detection efficacy and adaptability. The paper delineates the systematic progression of our work, encompassing meticulous data preprocessing, strategic feature selection, rigorous model training, and the deployment of an intuitive web application. Evaluative measures are employed on real-time network traffic datasets, affirming the model's proficiency in discerning Botnet activities with notable accuracy and reliability. Our work introduces an approach to Botnet detection leveraging machinelearning which increases the detection accuracy underscoring the efficacy of the proposed approach.
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