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Machine learning Approaches for Accurate Energy Content Prediction in Foods Using Nutritional Data 2

Machine Learning Approaches for Accurate Energy Content Pred...

引用

2nd international conference on computing and Data Analytics, ICCDA 2024

作者： Wickramasinghe, Nishan Hasan, Raza Ahmad, Shakeel Mahmood, Salman Solent University Department of Science and Engineering Southampton United Kingdom Nazeer Hussain University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798331542573

This study marks a step toward more effectively translating nutritional information to inform public health policy as well as individual dietary choices. Motivated by the increase in diet-related health issues, this research aims to analyze a comprehensive nutritional dataset to uncover valuable insights. Using the USDA National Nutrient Database, the study employs data preprocessing to clean the data, exploratory data analysis to identify hidden patterns, and various machine learning models to predict nutritional values. The results demonstrate the usefulness of these models in explaining the composition data and highlight a range of trends and relationships within the observed amounts. The discussion emphasizes that these findings could be instrumental in guiding health professionals and policymakers toward healthier dietary guidelines. The significance of this research lies in its potential to advance public health through more sophisticated nutritional recommendations. © 2024 IEEE.

关键词： Data Analytics Food Nutrition Machine learning

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Enhancing Pharmacokinetic Modeling with Fractional-Order Kinetics and Deep learning 14

Enhancing Pharmacokinetic Modeling with Fractional-Order Kin...

引用

14th IEEE international conference on Control System, computing and engineering, ICCSCE 2024

作者： Zou, Junting Arshad, Mohd Rizal Wang, Ziyan Universiti Sains Malaysia School of Electrical and Electronic Engineering Nibong Tebal Penang14300 Malaysia Osaka Metropolitan University Graduate School of Economics Osaka599-8531 Japan

ISBN: (纸本)9798350364507

In this study, we present an innovative pharmacokinetic modeling that combines fractional order kinetics with deep learning techniques to improve the prediction accuracy of drug concentration distribution in biological systems. Traditional pharmacokinetic models rely on ordinary differential equations, which often fail to accurately predict complex drug behaviors. To address these limitations, we develop a fractional-order pharmacokinetic model that can more accurately represent the memory and genetic properties of biological systems. To complement this, we combine fractional-order pharmacokinetics with the predictive capabilities of Long Short-Term Memory (LSTM) models in recurrent neural networks (RNNs). Synthetic datasets were first generated by selecting appropriate pharmacokinetic parameters to simulate a range of drug behavior scenarios. The dataset was then used to train and optimize the LSTM model, aiming to improve the prediction of the fractional-order model and thus achieve higher accuracy. Compared to traditional models, our results show significantly better prediction accuracy, with lower root mean square error (RMSE) and higher coefficient of determination (R2) values across time. This study not only highlights the potential of combining fractional-order kinetics with deep learning to improve pharmacokinetic models, but also opens avenues for more personalized and accurate drug therapy planning. © 2024 IEEE.

关键词： Prediction models

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An Explainable AI-Based Automated Acne Diagnosis Using Transfer learning and DenseNet121 10

An Explainable AI-Based Automated Acne Diagnosis Using Trans...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Paluri, Krishna Veni Gupta, Neha Mishra, A.K. Gupta, Ashish Nain, Garima School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India ABV-IIITM Department of Information Technology Madhya Pradesh Gwalior India

ISBN: (纸本)9798350384369

This study presents an automated acne detection method that uses transfer learning on the improved DenseNet121 model. In the improved model architecture, the head of the DenseNet121 model is replaced by customized layers leveraging dropout, batch normalization, average pooling, and dense layers. It is showcased that the presented architecture attained the highest testing accuracy of 92.13% compared to the models currently widespread in the literature (such as VGG16, ResNet50, VGG19, InceptionV3, and InceptionResNetV2). Furthermore, for the automatic visualization of acne, two visualization techniques are utilized, namely: LIME (Local Interpretable Model-Agnostic Explanations) as an Explainable Artificial Intelligence (XAI) attribute for improved interpretability in model predictions and the Streamlit framework for creating a user-friendly deep learning web application. © 2024 IEEE.

关键词： Visualization

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Socially Appropriate Robot Planning in Dynamic, Unseen Construction Environments

Socially Appropriate Robot Planning in Dynamic, Unseen Const...

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ASCE international conference on computing in Civil engineering 2023: Data, Sensing, and Analytics, i3CE 2023

作者： Kim, Yeseul Yarovoi, Andrew Han Ahn, Yong Cho, Yong K. School of Civil and Environmental Engineering Georgia Institute of Technology United States Institute for Robotics and Intelligent Machines Georgia Institute of Technology United States Dept. of Smart City Engineering Hanyang Univ. Korea Republic of

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

With the advancement of robotic technologies, an increasing number of robots are envisioned to be deployed in various human-occupied environments such as construction job sites. Although navigation in a static environment is well studied, navigation in such a dynamic environment is still a challenging problem. In particular, construction environments present many challenges as they are often densely populated with both static and dynamic objects (e.g., stocked materials, workers, and equipment). Thus, robots in the construction environment should be capable of adapting to frequent layout changes and dynamic human movements and producing efficient paths in unknown, dynamic environments accordingly. Given the safety-critical nature of the construction environment, it is also critical not to collide, distract, or interfere with workers when robots maneuver around human workers in the workplace. To accommodate this need, this study proposes a Reinforcement learning (RL)-based navigation model that enables robots to adhere to appropriate safety proxemic considerations aligned with the social work convention of construction workplaces while following a globally planned trajectory. The result showed that our model could secure the respective minimum distance in contrast to the baseline model, which suffered in complex environments. These findings will contribute to building future construction mobile robots with social intelligence that can produce socially compliant and safe behaviors. © international conference on computing in Civil engineering *** rights reserved.

关键词： Navigation

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Reinforcement learning for Autonomous Vehicles 1

Reinforcement Learning for Autonomous Vehicles

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vinod Kumawat, Parwati Kumar, Pankaj Yaminipriya, S. Chamarthi, Subbarao Kumar, Jitendra SLP College Dept of CSE Gwalior India Madhav University Dept. of Commerce & Management Pindwara India Dhanalakshmi Srinivasan College of Engineering Dept. of Biomedical Engineering Coimbatore India Lingayas Vidyapeeth Dept. of Mechanical Engineering Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

Reinforcement learning (RL) has emerged as a transformative technology for autonomous vehicles, enabling sophisticated decision-making systems that enhance driving safety, efficiency, & adaptability. This paper explores the application of RL algorithms in the context of autonomous vehicle control, focusing on the development of intelligent agents capable of learning optimal driving policies through interaction with complex environments. The study reviews various RL techniques, including Deep Q-learning, Policy Gradient methods, & Actor-Critic frameworks, evaluating their effectiveness in addressing key challenges such as real-time decision making, dynamic environment adaptation, & multi-agent interactions. Emphasis is placed on the design of reward functions, exploration strategies, & simulation environments, which are crucial for training robust & reliable autonomous driving systems. Case studies demonstrate the application of RL in scenarios such as lane-keeping, adaptive cruise control, & collision avoidance. The paper also discusses advancements in simulation platforms & hardware acceleration that facilitate scalable RL experiments. By integrating theoretical insights with practical implementations, this work provides a comprehensive overview of current developments in RL for autonomous vehicles & identifies future research directions aimed at overcoming limitations & achieving safer, more efficient. © 2024 IEEE.

关键词： Reinforcement learning

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Plant Disease Recognition Using Different CNN Models 14

Plant Disease Recognition Using Different CNN Models

引用

14th international conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Gupta, Shivam Gilotra, Shrey Rathi, Shivam Choudhury, Tanupriya Kotecha, Ketan School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Graphic Era Deemed to Be University Cse Dept. Uttarakhand Dehradun248002 India Symbiosis Institute of Technology Symbiosis International University Symbiosis Centre for Applied Artificial Intelligence Pune411045 India Cse Dept. Maharashtra Pune412115 India

ISBN: (纸本)9798350344837

The research paper highlights the remarkable threats that plant diseases pose to global agricultural productivity and food security. The early detection and accurate recognition of these diseases are crucial for the effective management of plants. Deep learning models, such as CNN, Resnet34, Resnet50, and VGG16, have shown propitious results in computer vision tasks, including plant disease recognition. The study explores the effectiveness of these models using a comprehensive dataset consisting of images of healthy and diseased plants. The pretrained versions of the models are fine-tuned using transfer learning techniques with performance evaluation based on metrics like accuracy, precision, recall and f1 score. Comparative analysis also identifies each model's strengths and weaknesses in classifying plant diseases, considering factors like model architecture depth and complexity. The overall findings of this research contribute towards the development of automated plant disease recognition systems integrated into real-time monitoring systems. © 2024 IEEE.

关键词： Deep learning

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Gamified Constructivist teaching in Metaverse: Revolutionizing Language learning in University via an Immersive Experience 2

Gamified Constructivist Teaching in Metaverse: Revolutionizi...

引用

2024 international conference on Metaverse computing, Networking, and Application

作者： Ng, Peter H. F. Har, Frankie T. K. Tai, Ken S. K. Leung, Winnie C. L. Chen, Peter Q. Zhou, Laura Lam, Joe K. H. Law, Helen K. W. Li, Chen Lam, Yan Yan Li, Qing Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hong Kong Polytech Univ English Language Ctr Hong Kong Peoples R China Hong Kong Polytech Univ Educ Dev Ctr Hong Kong Peoples R China Hong Kong Polytech Univ Dept Appl Biol & Chem Technol Hong Kong Peoples R China Hong Kong Polytech Univ Dept Hlth Technol & Informat Hong Kong Peoples R China Hong Kong Polytech Univ Dept Appl Social Sci Hong Kong Peoples R China Hong Kong Polytech Univ Chinese Language Ctr Hong Kong Peoples R China

ISBN: (纸本)9798331516000;9798331515997

Acquiring a second language at the university level can be a challenging endeavor, especially for adolescents who may experience apprehension about speaking and practicing in the presence of their peers. To surmount these obstacles, the utilization of the metaverse for language learning emerges as a viable strategy. However, the current landscape of metaverse platforms often reveals a deficiency in the functionalities required for efficient autonomous learning. In response to this predicament, we have developed a metaverse-based game, Questverse, integrated with ***, to foster an environment conducive to immersive learning activities. This platform not only facilitates interaction between instructors and learners but also introduces an innovative online game focused on stage speech training. Features such as customizable avatars and PolyU virtual coins have been incorporated to enhance student engagement. A distinguished feature of Questverse is the incorporation of a chatbot designed to support self-directed learning. This tool allows learners to hone their language abilities independently, with immediate feedback and performance assessments. Rigorous evaluations involving various student demographics have been undertaken, yielding optimistic outcomes. The findings affirm the platform's efficacy in augmenting language proficiency and bolstering learners' confidence in their language capabilities. A specialized questionnaire, GCTM, has been devised to assess further our course design's impact, which is grounded in constructivist teaching principles and game development. This learner-oriented approach enables us to glean insights from the students' perspectives on their experiences within the metaverse, thus providing a comprehensive understanding of the effectiveness of our educational strategies.

关键词： Metaverse Constructivist Gamification Language learning Higher Education

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Harnessing Medical Imaging Applications: Survey on 3D Deep learning Models for Image Segmentation 3

Harnessing Medical Imaging Applications: Survey on 3D Deep L...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Nambiar, Rajashree Nanjundegowda, Raghu Dept of Electronics and Communication Bengaluru India Dept of Robotics and Ai Engineering Nitte India Dept of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331528201

The domain of 3D medical image segmentation has progressed markedly with the incorporation of deep learning methodologies, offering critical instruments for accurate and quick examination of intricate medical pictures. Various research has created 3D convolutional neural networks (CNNs) designed to segment diverse anatomical and pathological features, tackling specific issues such as high dimensionality, class imbalance, and computing requirements. Innovative models such as DeepMedic, which implemented a multiresolution strategy for brain lesion identification, together with other U-Net adaptations, have attained remarkable outcomes in tumor and organ segmentation through the integration of contextual and spatial data. Recent advancements encompass models employing large-kernel (LK) attention modules, transformer-based architectures, and hybrid approaches such as UNETR++ with Efficient Paired Attention, all enhancing accuracy while optimizing computing efficiency. These developments highlight the potential of 3D deep learning for clinical applications;yet, challenges such as restricted data availability, substantial annotation expenses, and generalizability across varied datasets persist as avenues for additional investigation. © 2024 IEEE.

关键词： Convolutional neural networks

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Discretization-Based Ensemble Model for Robust learning in IoT 20th

Discretization-Based Ensemble Model for Robust Learning in I...

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20th EAI international conference on Mobile and Ubiquitous Systems - computing Networking and Services (EAI MobiQuitous)

作者： Namvar, Anahita Thapa, Chandra Kanhere, Salil S. UNSW Sydney Kensington NSW 2052 Australia Data61 Marsfield Sydney NSW 2122 Australia

ISBN: (纸本)9783031639913;9783031639920

The rapid proliferation of Internet of Things (IoT) devices has introduced new challenges in network management and security. While machine learning models hold promise for identifying these devices, they remain vulnerable to adversarial attacks, undermining their accuracy and reliability. This paper addresses the need for robust IoT device identification by proposing a novel approach: a discretization-based ensemble stacking technique. Thismethod harnesses both the protective properties of discretization and the generalization benefits of ensemble methods. Through extensive experimentation, we demonstrate the efficacy of our approach against various adversarial attacks, showcasing its potential to enhance the resilience and accuracy of IoT device identification models in dynamic and uncertain environments.

关键词： Adversarial Robustness IoT Discretization Ensemble

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Predictive Autoscaling for Containerized Applications Using Machine learning 1

Predictive Autoscaling for Containerized Applications Using ...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. School of Computer-Science and Engineering Sandip University Department of Computer Science & Applications Nashik India

ISBN: (纸本)9798350361780

As containerization continues to gain prominence in modern application deployment, the need for efficient autoscaling mechanisms becomes paramount to ensure optimal resource utilization and adherence to service level objectives. This research paper proposes a Predictive Auto-scaling framework leveraging machine-learning(ML) techniques to anticipate and proactively adjust the resources allotted to containerized applications. The aim is to enhance the scalability and responsiveness of applications running in dynamic and cloud-native environments. An auto-scaling technique with burst awareness for containerized applications is proposed in this research. This innovative method optimizes container utilization while meeting the Quality-of-Service requirement by taking into account a mix of both vertical and horizontal skills. The proposed proactive autoscaling strategy addresses the challenges posed by unpredictable workloads and varying application demands, fostering adaptability to dynamic environments. By highlighting the significance of machine learning in improving the predictability and effectiveness of resource scaling for containerized systems, this study adds to new work of research on auto-scaling methodologies. © 2024 IEEE.

关键词： Containers

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