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Enhancing Road Safety: An Intelligent Drowsiness Detection System Based on Deep Neural Networks 7

Enhancing Road Safety: An Intelligent Drowsiness Detection S...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nalavade, Jagannath E. Sachdeo, Rajneeshkaur Kale, Dattatray R. Buchade, Amar Subhedar, Mansi Shinde, Swapnil K. Mit Art Design and Technology University Computer Science and Engineering Pune India BRACT's Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India Pillai Hoc College of Engineering and Technology Electronics and Computer Science Rasayani India

ISBN: (纸本)9798331527822

The aim of this research is to create an intelligent vehicle system that can effectively mitigate the risks associated with drowsy driving, thereby preventing road accidents. Various factors like driver exhaustion, challenging road conditions, and adverse weather contribute to motor vehicle crashes, leading to over 1.35 million deaths annually, as estimated by entities such as the World Health Organization (WHO). To achieve this, the development of a robust warning system is crucial. In this study, we proposed an alert system to detect drowsy drivers. It utilizes a video stream process (VSP) to analyze the gaze's eye-aspect ratio (EAR) and geometric distance through an innate reflex model. Additionally, a facial landmark-based algorithm is employed for accurate eye recognition. When driver drowsiness is detected, the system issues a warning message, along with crash impact and location information, and activates the voice surveillance system. The suggested technique establishes a non-invasive approach for evaluating driver weariness in connection with accident severity. The camera, the fundamental element of this system, continuously acquires facial landmarks recognized by designated locations and analyzes them. Drivers who are distracted and have poor lighting may show signs of sleepiness. © 2024 IEEE.

关键词： Deep neural networks

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Multi-Task Learning Frameworks to Classify Food and Estimate Weight From a Single Image 20

Multi-Task Learning Frameworks to Classify Food and Estimate...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Siwathammarat, Pakin Jesadaporn, Panas Chawachat, Jakarin Chiang Mai University Data Science Research Center Faculty of Science Thailand Chiang Mai University Faculty of Medicine Department of Internal Medicine Thailand Faculty of Science Department of Computer Science Chiang Mai Thailand

ISBN: (纸本)9798350300505

Usually, elderly patients in hospitals suffer from malnutrition because they are unable to consume food as prescribed by doctors or nutritionists. Analyzing food intake is labor-intensive and time-consuming. Therefore, machine learning is used to analyze the food intake. Major food analysis tasks include food classification and food weight estimation. The basic machine learning approach to this problem is to combine a food classification model with a food weight estimate model sequentially. When we deployed it, we found that a large amount of memory and models were required. One solution is to use multi-task learning. In this study, we proposed multi-task learning frameworks that could recognize food and predict weight based on a single image. The performance of our frameworks was compared to the baseline models, which only utilized either regression or classification. Although baseline accuracy is higher, our framework has MAPE values that are lower than the baseline. To improve the performance, we explored different approaches for weighting loss, including manual weighting and auto weighting, using uncertainty and auxiliary tasks. From the experiment, our results showed that our multi-task learning framework that adjusted the weight of loss using auxiliary tasks outperformed the baseline models in terms of MAPE and Accuracy. Moreover, we demonstrate our framework when scaling up the backbone from ResNet50 to ResNet101 and ResNet152. © 2023 IEEE.

关键词： Image classification

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Modified Recommender Algorithm based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Methodology with Entropy Weighting Method (EWM) 4

Modified Recommender Algorithm based on Technique for Order ...

引用

4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Pacaon, Mark Reden S. Ballera, Melvin A. Technological Institute of the Philippines Graduate Program Manila Philippines Technological Institute of the Philippines Department of Computer Science Manila Philippines

ISBN: (纸本)9798350376111

This research exhibits an algorithm adapted from the Technique for Order of Preference by Ideal Solution (TOPSIS) methodology, integrating the Entropy Weighting Method (EWM) for objective and efficient recommendation processes. Decision-making methodologies have indicated gaps of subjective influence affecting performance values on several implementations with additional limitations on the flexibility of handling complex problems that require multiple factors while maintaining an acceptable running time. The study takes this into account and formulates an adaptable and efficient framework that offers objective and data-driven recommendations for diverse situations. Future endeavors include subjecting the framework to real-life scenarios and testing its effectiveness. © 2024 IEEE.

关键词： decision-making entropy weighting method modified algorithm multicriteria TOPSIS

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Sequential Image Storytelling Model Based on Transformer Attention Pooling 38

Sequential Image Storytelling Model Based on Transformer Att...

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Malakan, Zainy M. Hassan, Ghulam Mubashar Mian, Ajmal The University of Western Australia Department of Computer Science and Software Engineering Perth Australia Umm Al-Qura University Department of Data Science Makkah Saudi Arabia

ISBN: (纸本)9798350370515

The Visual Storytelling Task (VST) extends beyond describing a single image, such as image captioning, to sequential image descriptions in the form of a coherent story. However, such descriptions present challenges in handling varying language styles, relational role-modeling, consistency, and events not evident in individual images. A common limitation in existing approaches is their inability to fully describe relations and visual changes, leading to stories devoid of linguistic cohesion between multiple sentences. To address this challenge, we introduce a novel framework, Sequential Image Storytelling Model (SISM), based on the Transformer architecture. Our model contextualizes input images by dividing them into multiple 16 × 16 patches and associates them with language content using the encoder-decoder technique. It incorporates cross-attention and attention pooling to identify the most relevant relationships between input images. Our proposed model achieves state-of-the-art performance on the recently published SSID dataset and performs competitively on VIST dataset, achieving the top score on BLEU-1 metric. © 2023 IEEE.

关键词： Modeling languages

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Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints 41

Near-Optimal Reinforcement Learning with Self-Play under Ada...

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41st International Conference on Machine Learning, ICML 2024

作者： Qiao, Dan Wang, Yu-Xiang Department of Computer Science University of California UCSB Santa Barbara United States Halıcıoglu Data Science Institute University of California UCSD San Diego United States

We study the problem of multi-agent reinforcement learning (MARL) with adaptivity constraints - a new problem motivated by real-world applications where deployments of new policies are costly and the number of policy updates must be minimized. For two-player zero-sum Markov Games, we design a (policy) elimination based algorithm that achieves a regret of Oe(√H3S2ABK), while the batch complexity is only O(H + log log K). In the above, S denotes the number of states, A, B are the number of actions for the two players respectively, H is the horizon and K is the number of episodes. Furthermore, we prove a batch complexity lower bound Ω(logHA K + log log K) for all algorithms with Oe(√K) regret bound, which matches our upper bound up to logarithmic factors. As a byproduct, our techniques naturally extend to learning bandit games and reward-free MARL within near optimal batch complexity. To the best of our knowledge, these are the first line of results towards understanding MARL with low adaptivity. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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QoE Comparison between Multi-User OFDMA and Reliable Groupcast for Wireless LAN Audiovisual Transmission

QoE Comparison between Multi-User OFDMA and Reliable Groupca...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Deguchi, Daiki Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350364866

This paper compares video and audio QoE of OFDMA multi-user transmission and reliable groupcast over wireless LAN. We assume video and audio transmission to several terminals simultaneously. As a reliable groupcast method, we employ Unsolicited Retry, a technique of IEEE 802.11aa GroupCast with Retries. We utilize IEEE 802.11ax for OFDMA transmission. We evaluate application-level QoS by computer simulation. We then assess QoE through a subjective experiment with video and audio streams generated by the output timing obtained from the simulation. We notice that each method has a situation to be applied appropriately. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Machine Learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. These algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) Machine learning Neural Network (NN)

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Decision Support Framework: Integrating Analytic Hierarchy Process with Support Vector Machine and Top-K Algorithms

Decision Support Framework: Integrating Analytic Hierarchy P...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： De Jesus, Mervin Jommel T. Ballera, Melvin A. Graduate Program Technological Institute of the Philippines Manila Philippines Department of Computer Science Technological Institute of the Philippines Manila Philippines

ISBN: (纸本)9798350353464

Effective recommendation systems are essential for supporting decision-making in various fields in this age of abundant information. This paper presents a novel framework for a Modified Recommender Algorithm that combines Top-K and Support Vector Machine (SVM) algorithms with the Analytic Hierarchy Process (AHP). Traditional decision-making frameworks such as AHP are susceptible to subjectivity and inconsistencies, necessitating innovative approaches to improve their reliability and effectiveness. The objective is to improve recommendation systems' efficacy and precision by integrating sophisticated machine-learning techniques with resilient decision-making approaches. The proposed framework establishes the hierarchical structure of decision elements and prioritizes alternatives according to user preferences by utilizing AHP, a potent multi-criteria decision-making tool. Support Vector Machine (SVM) is then used as a text classification model to enrich the feature space for recommendations by classifying textual data into pertinent classes. In addition, the framework applies Top-K Algorithms to select the most pertinent alternatives based on SVM classification output, thereby refining the recommendation results. Top-K Algorithms improve overall recommendation accuracy and relevance by iteratively filtering and ranking alternatives. This research advances the field of data science by providing a thorough framework that combines innovative machine learning techniques with conventional decision-making methodologies to address the challenges of recommendation systems in complex and dynamic environments. ©2024 IEEE.

关键词： Analytic hierarchy process

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Understanding "mutes" in Social Virtual Reality 25

Understanding "mutes" in Social Virtual Reality

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Chen, Qijia Bellucci, Andrea Cai, Jie Nelimarkka, Matti Jacucci, Giulio Department of Computer Science University of Helsinki Helsinki Finland Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Leganés Spain Department of Computer Science and Technology Tsinghua University Beijing China Centre for Social Data Science University of Helsinki Helsinki Finland Department of Computer Science Aalto University Helsinki Finland Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (纸本)9798400713941

In social Virtual Reality (VR), particularly within VRChat, a significant group of users often referred to as "mutes"refrain from voice communication. This study analyzes 4212 discussion entries, including both original submissions and comments, from the r/VRchat subreddit to explore the experiences and reasons behind this practice. Our findings indicate that muteness is an integral aspect of social VR culture, yet mute users face challenges, including exposure to abusive behaviors and communication barriers in a fast-paced environment. Factors of social VR like harassment, heightened social anxiety from the immersive presence, and the complexities of identity management can discourage voice communication, leading many to adopt "muteness"as a response. This behavior can be seen within the broader context of social disability, challenging normative communication assumptions. We highlight the risks of generalizing marginalized communities and emphasize the need for further research to address and support the unique needs of these groups in social VR spaces. © 2025 Copyright held by the owner/author(s).

关键词： Disabled persons

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning 2

Revolutionizing Machine Learning Security: The Role of Quant...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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