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Emergency Vehicle Prediction Using Deep Convolution Neural Network for Safety Purposes 5

Emergency Vehicle Prediction Using Deep Convolution Neural N...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Konda, Rajasekhar Chandramouli, Deepak Mohanakumar Ayyannan, Manimuthu Ml Platform Data Platform and Distributed Systems CA United States AI/ML and Data Expert CA United States

ISBN: (纸本)9798350321425

Ambulances, fire trucks, and police cars are essential in each town. These vehicles are designed and furnished with the equipment required to respond quickly to emergencies and assist those in need. The short reaction time and state-of-the-art medical equipment on board could be the difference between life and death for patients. This research seeks to create and assess two Deep Neural Networks (DNN) that may be used to forecast the presence of an Emergency Vehicle (EV) in a congested route by identifying the sound of the siren to prevent such worse outcomes in the future. For this process, three separate car noises are obtained from Kaggle as a dataset. This dataset includes sounds from fire trucks, ambulances, and regular cars. Then, using the feature extraction method known as Mel-frequency Cepstral Coefficients (MFCC), the primary features from the sounds are retrieved. This method was created exclusively for feature extraction from sound-based data. Two alternative Deep Learning (DL) and Convolutional Neural Networks (CNN) are created with two different techniques. The ResNet method and its combination with the XGBoost algorithm were both employed in this investigation. The two models are then trained using the resulting dataset's feature extractions. For both algorithms, accuracy starts relatively low and steadily rises as the number of epochs rises. When compared, the graph for the loss value likewise looks comparable. The loss graph of the combination algorithm is also superior, and it achieves high accuracy in fewer epochs than the solo algorithm. Moreover, validation exhibits a performance akin to training. Even though the combination algorithm performed far better than the solo algorithm, the model still needs to be tested one last time to establish the final parameters. Results from testing are also tabulated and contrasted. In the end, it is discovered that the hybrid algorithm is in many ways superior to the solo algorithm. Future versions of the model

关键词： Feature extraction

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Emergency Vehicle Prediction Using Deep Convolution Neural Network for Safety Purposes

Emergency Vehicle Prediction Using Deep Convolution Neural N...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Rajasekhar Konda Deepak Mohanakumar Chandramouli Manimuthu Ayyannan ML Platform Data Platform and Distributed Systems California USA AI/ML and Data Expert California USA

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AI and IoT based Intrusion Detection System for Cybersecurity 5

AI and IoT based Intrusion Detection System for Cybersecurit...

引用

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Konda, Rajasekhar Ayyannan, Manimuthu Chandramouli, Deepak Mohanakumar Software Engineering Manager & Ieee Senior Member Bay area San FranciscoCA United States AI/ML and Data Expert | Ieee Member Sr. Software Engineering Manager Bay Area San FranciscoCA United States Data Platform and Distributed Systems Sr. Software Engineer | Ml Platform Bay Area San JoseCA United States

ISBN: (纸本)9798350321425

A network's traffic can be monitored by an Intrusion Detection System (IDS), which then sends out warnings if any malicious behavior is detected. It's a program that checks if a website link leads to malicious content or if there was a policy violation on the network. As a case study for IDS, identifying phishing websites has been performed. One of the most effective tools for hackers to carry out a variety of security attacks is the widespread distribution of fake website Uniform Resource Locators (URLs) via messages, email, etc., with the goal of fooling unaware consumers into providing over sensitive information or worse. Traditional methods for detecting phishing websites, such as employing blacklists, are used to make predictions about phishing URLs. Traditional solutions based on blacklists are not effective at predicting many phishing websites, because so many new ones are created and released on the Internet every day. Three distinct Artificial Intelligence (AI) models-Random Forest (RF), Naive Bayes (NB), and XGBoost-are used to accurately detect dangerous URLs. Both the authentic and the fraudulent URLs were obtained from various sources. The AI model is trained with the retrieved relevant features. Following this, performance indicators are used to assess the quality of the AI models. According to the outcome of the testing data, the XGBoost model achieved a maximum accuracy of 96.98%, which is higher than the accuracy of the remaining models tested. Then the XGBoost model has been finalized and deployed as a website, anyone can immediately recognize the phishing URL. © 2023 IEEE.

关键词： Websites

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AI and IoT based Intrusion Detection System for Cybersecurity

AI and IoT based Intrusion Detection System for Cybersecurit...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Rajasekhar Konda Manimuthu Ayyannan Deepak Mohanakumar Chandramouli Software Engineering Manager & IEEE Senior Member San Francisco Bay area California USA Sr. Software Engineering Manager AI/ML and Data Expert | IEEE member San Francisco Bay Area California USA Sr. Software Engineer | ML Platform Data Platform and Distributed Systems San Jose Bay Area California USA

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