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A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks

A Novel Approach for Driver Drowsiness Detection using Deep ...

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Women in Innovation, Technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Sharon K V Natarajan B P. Madhumitha A Dhanamathi Prabu S S Saranya Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Dept. of Artificial Intell. and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Dept. of CSE K. Ramakrishnan College of Engineering Tiruchirappalli Tamil Nadu India

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver’s eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions.

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Exploring the Potential of IoT in Healthcare: Challenges and Opportunities

Exploring the Potential of IoT in Healthcare: Challenges and...

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Technological Advancements in computational sciences (ICTACS), International Conference on

作者： Navneet Verma Sumit Rana Shivani Gaba Shally Nagpal Dept. of Computer Science & Engg. Paniapt Institute of Engineering and Technology Panipat Haryana India Dept. of Artificial Intelligence and Data Science Paniapt Institute of Engineering and Technology Panipat Haryana India

Due to the propagation of IoT technologies Healthcare cannot remain outside of this paradigm. In latest years, there has been a lot of interest in the Internet of Things (IoT) technology to relieve the strain on healthcare systems brought on by the ageing population and the rise in persistent illnesses. One of the main obstacles preventing advancement in this field is standardization or global connectivity between the Internet of Things (IoT) and the medical surroundings. All from electrical engineers to data engineers have a significant problem in having to integrate everything in a global setting. The difficulties that healthcare IoT confronts, such as security, privacy, and less-energy procedures, are discussed, and suggestions are provided for future research paths. This paper is written to gather data on various Internet of Things architectures now used in Healthcare and analyse how well different academics have applied it.

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Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees

Phishing URLs Using Machine Learning Hybrid Stacking Classif...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Chengamma Chitteti Tanguturi Sai Divya Sree K. Reddy Madhavi Pikkili Pooja Sannapaneni Jayanth Middi Harshavardhan Reddy Dept. Of Data Science Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupathi India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupathi India Dept. Of AI & ML Mohan Babu University Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Within the dynamic field of cybersecurity threats, phishing remains a prevalent and sophisticated tactic employed by malicious actors to deceive consumers. This paper presents a novel approach that leverages the strengths of Random Forest, ExtraTrees, and XGBoost in a machine learning framework to enhance the detection of phishing URLs through a hybrid stacking classifier. The suggested method combines the best features of all three ensemble learning techniques to provide a phishing detection model that is reliable and precise. An ensemble of classifiers is formed by combining XGBoost, which is well-known for its boosting abilities, with the highly randomized ExtraTrees method and the adaptable Random Forest algorithm. By using the various decision limits of each base learner, the hybrid stacking model combines their particular capabilities in the best possible way to provide higher prediction performance. Preprocessing is done on the training dataset to extract pertinent characteristics such as content, URL structure, and contextual data. The efficacy of the suggested method in detecting phishing URLs is assessed by extensive testing on an extensive dataset. The results indicate how well the hybrid stacking classifier performs in comparison to separate classifiers and highlight the possibility for improved phishing detection in practical situations. The study has importance as it presents a novel and efficient machine learning-based approach to counteract the rising danger of phishing assaults, hence propelling the area of cybersecurity forward.

关键词： Uniform resource locators Adaptation models Accuracy Phishing Prevention and mitigation Stacking Malware

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BILEVEL LEARNING OF 1 REGULARIZERS WITH CLOSED-FORM GRADIENTS (BLORC) 47

BILEVEL LEARNING OF 1 REGULARIZERS WITH CLOSED-FORM GRADIENT...

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2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022

作者： Ghosh, Avrajit McCann, Michael T. Ravishankar, Saiprasad Dept. of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Theoretical Division Los Alamos National Laboratory Los AlamosNM United States Dept. of Biomedical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9781665405409

We present a method for supervised learning of sparsity-promoting regularizers, which are a key ingredient in many modern signal reconstruction approaches. The parameters of the regularizer are learned to minimize the mean squared error of reconstruction on a training set of ground truth signal and measurement pairs. Training involves solving a challenging bilevel optimization problem with a nonsmooth lower-level objective. We derive an expression for the gradient of the training loss using the implicit closed-form solution of the lower-level variational problem given by its dual problem, and provide an accompanying gradient descent algorithm (dubbed BLORC) to minimize the loss. Our experiments on simple natural images and for denoising 1D signals show that the proposed method can learn meaningful operators and the analytical gradients are calculated faster than standard automatic differentiation methods. While the approach we present is applied to denoising, we believe that it could be adapted to a wide variety of inverse problems with linear measurement models, thus giving it applicability in a wide range of scenarios. © 2022 IEEE

关键词： Signal reconstruction

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Multi-Language Medical Symptoms Analyzer and Hospital Locator Chatbot

Multi-Language Medical Symptoms Analyzer and Hospital Locato...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Kasarapu Ramani Malepati Sai Deepthi N. Naveen Chintala Jeevan Raju Kandati Krishna Dept. of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of IT Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Not every hospital may have a help desk to respond to patient queries regarding the disease prediction based on their symptoms and suggesting appropriate department or medicine, providing precautions for that disease in multiple languages. An automated system, such as a chatbot, can effectively address this issue. The medical chatbot is trained using Hybrid Deep Neural Network-Recurrent Neural Networks (DNN-RNN). The user's query accepted through user interface is pre-processed using Natural Language Processing (NLP) and based on the symptoms disease is predicted using wikipedia summary dynamically. The chatbot is user-friendly, and allows the users to interact in three different languages-English, Hindi and Telugu. It also enables users to enter the location in text, and it using Geopy library, Google Places API, and Folium library hospitals' location information is displayed in the user-specified area. The performance of the proposed method is evaluated using accuracy and loss function. This proposed method is a highly effective learning process, making the chatbot a reliable tool for medical queries and disease prediction based on symptoms.

关键词： Hospitals Neural networks Encyclopedias User interfaces Chatbots Libraries Internet

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Precise Diagnosis of Heart Disease with Stacking Classifier

Precise Diagnosis of Heart Disease with Stacking Classifier

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Emerging Technology (INCET), International Conference for

作者： V. Jyothsna B. Poojitha A.Sai Divya B. Ganesh D. Srinija Department of Data Science Mohan Babu University Tirupati India Dept. of information technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Heart disease stands as a significant global health challenge, marked by a worrying rise in prevalence and substantial mortality rates. Despite the wealth of healthcare data, early detection of heart conditions remains elusive. This endeavor aims to address this issue by crafting a predictive model that integrates individual medical histories and lifestyle factors. The proposed methodology centers on the use of a Stacking classifier for heart disease prediction. To gauge the model's efficacy, the Framingham Heart Disease dataset is employed, sourced from the widely recognized Kaggle repository, endorsed by numerous researchers. Evaluation encompasses ensemble model assessments, incorporating critical metrics such as sensitivity, specificity, accuracy, and f-measure. Furthermore, the investigation delves into the application of ensemble methods for forecasting heart disease incidence over a decade, aiming to contribute to more effective strategies for early detection and management. Through the fusion of advanced modeling techniques and comprehensive datasets.

关键词： Heart Accuracy Sensitivity Stacking Predictive models Prediction algorithms Robustness

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Real-Time Stress Level Monitoring Using IOT

Real-Time Stress Level Monitoring Using IOT

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Asha Nandini Patil B M Preeti Laxmi Dept. of Artificial Intelligence and Data Science Sharnbasva University Kalaburagi Karnataka India Dept. of Computer Science and Engineering Sharnbasva University Kalaburagi Karnataka India Dept. of Electrical and Electronics Engineering Sharnbasva University Kalaburagi Karnataka India

Stress is a prevalent and debilitating issue in contemporary society, contributing to a range of physical and mental health problems. As our lives become increasingly fastpaced and interconnected, addressing stress effectively has become imperative. This introduces a pioneering approach to tackle challenge through real-time stress level monitoring utilizing the Internet of Things (IoT). We propose a holistic system that merges IoT sensors, advanced data analytics, and machine learning techniques to provide continuous stress assessment and management solutions. The data is collected through the designed model then preprocessed, and essential features are extracted to construct a predictive model. The heart of the system is a machine learning model, which constantly updates itself based on user-specific stress patterns, yielding highly accurate real-time stress level estimations. Users are provided with actionable feedback and personalized recommendations, ranging from deep breathing exercises to lifestyle adjustments. Extensive experimentation demonstrates the system's robustness, with stress level predictions boasting a mean absolute error of under 5%.

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Secured Home Automation with Voice Recognition Using ML and IoT Devices

Secured Home Automation with Voice Recognition Using ML and ...

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Computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Sovan Bhattacharya Anisha Singha Tanmoy Sural Rishi Raj Pandey Rohit Paul Shubhranshu Gorai Saibal Majumder Dola Sinha Chandan Bandyopadhyay Dept. CSE (Data Science) Dr. B. C. Roy Engineering College Durgapur India Dept. Electrical Engineering Dr. B. C. Roy Engineering College Durgapur India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

In home, We utilize Gaussian Mixture Model to train voice recognition, and subsequently implement it in Internet of Things devices. We utilize these machine learning frameworks to enhance the precision, adaptability, and robustness of the system in order to mitigate security breaches. Hence, the main objective of the study is to develop a secure authentication system that is on one hand is more precise and on its other is user-friendly, with speech recognition playing a significant role to develop a next level home automation technologies.

关键词： Computer vision Home automation Authentication Speech recognition Machine learning Robustness Internet of Things Security Machine intelligence Gaussian mixture model

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Illumination Effects on Deep Fake Detection: Performance Comparison of CNN Architectures

Illumination Effects on Deep Fake Detection: Performance Com...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Sai Bhavya Yangoti Rohith Dumpala Vijay Punyamurthy Ravi Kumar Jatoth dept. of Artificial Intelligence and Data Science Indian Institute of Information Technology Design and Manufacturing Kurnool India dept. of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

While AI has many advantages, it also has a few drawbacks associated with “deep fakes.” Deep fakes involve the creation of very realistic videos and images realized through technologies that work with facial and voice modalities for deceptive purposes. Researchers have developed various techniques to detect these manipulated videos and images, employing complex neural network models such as convolutional neural networks (CNNs). However, deep fakes are very difficult to identify due to lighting and shadow effects that may hide some signs of manipulation. No one technique of detection could have the ability of identifying the tampered images under all kinds of illumination conditions. This paper compares different CNN models-ResNet50, EfficientNet, and MobileNetV2-to improve detection accuracy so as to be more resistant to new deep fake techniques, in particular under different illumination conditions. The importance of the study lies in arriving at which of the considered CNN models turns out to be most accurate under a specific illumination effect. Such an understanding could, therefore, improve the results of detection even when applied to cases of tampered faces in fake Aadhaar detection and fake id detection, thereby clearly bringing out the usefulness of the research in practice.

关键词： Resistance Deepfakes Accuracy Lighting Computer architecture Software Convolutional neural networks Reliability Artificial intelligence Protection

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Noninvasive assessment of spatio-temporal recurrence in atrial fibrillation 28

Noninvasive assessment of spatio-temporal recurrence in atri...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Bonizzi, Pietro Zeemering, Stef van Rosmalen, Frank Schotten, Ulrich Karel, Joël Dept. of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Dept. of Physiology Maastricht University Maastricht Netherlands Dept. of Biomedical Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9789082797053

Propagation of Atrial Activity during atrial fibrillation (AF) is a complex phenomenon characterized by a certain degree of recurrence (periodic repetition). In this study, we investigated the possibility to detect recurrence noninvasively from body surface potential map recordings in patients affected by persistent AF, and localize this recurrence both in time and space. Results showed that clusters of recurrence can be identified from body surface recordings in these patients. Moreover, the number of clusters detected and their location on the top-right of the back of the torso were significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. This suggests that noninvasive quantification of recurrence in persistent AF patients is possible, and may contribute to improve patient stratification. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Noninvasive medical procedures

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