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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Guo, Zhongsui Javadi, Bahman Singh, Sonit Sowmya, Arcot School of Computer Science and Engineering Unsw Sydney Australia School of Computer Data and Mathematical Sciences Western Sydney University NSW Australia

ISBN: (纸本)9798350379037

Dietary management plays a crucial role in maintaining long-term health, preventing diseases and aiding in recovery, particularly amidst the increasing prevalence of chronic conditions such as hypertension, cardiovascular diseases and diabetes. To achieve best practice in dietary management, providing insights into calorie intake based on the food items that people are eating is pivotal. In this paper, we propose a methodology to achieve this in three phases: instance segmentation, volume estimation and calorie calculation. Building on the foundations of Food Segment Anything Model (FoodSAM), we propose Masked-enhanced FoodSAM (MFoodSAM) focussing on enhancing semantic and instance masks and providing better food identification. MFoodSAM incorporates ResNet50 as a background model, improving its ability to separate non-food elements and reducing dependency on merging thresholds. We follow a coarse-to-fine segmentation approach to create detailed and semantically enriched masks, providing accurate instance and panoptic segmentation masks. After we accurately identify food items, we estimate food volume by calculating the pixel count from the food instance masks and combine this data with information from the USDA National Nutrient database and Nutritiondata to compute the caloric content of the food. We applied the proposed method on both a public and a private food dataset, and obtained state-of-the-art results, with mean IoU of 87.99;mean Accuracy of 95.55;average Acc of 94.85 on MyFood dataset, and mean IoU of 82.38;mean Acc of 92.85;average Acc of 94.66 on UNSW-Food dataset. This intelligent food monitoring system provides users with precise caloric information, facilitating more effective nutrition monitoring and contributing to improved health outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN 8

A Survey on Deep Learning Techniques for Secure and Accurate...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Machine Learning in Healthcare: Prognosis Heart Disease Prediction 4

Machine Learning in Healthcare: Prognosis Heart Disease Pred...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Ramnath, M. Revathi, B. Selva Birunda, S. Usharani, C. Ramana, R. Tyson Masillamani, J. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Thamirabharani Engineernig College Department of Computer Science and Engineering Tirunelveli India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

The heart, being a crucial organ, necessitates meticulous care. Accurate information is essential for identifying heart-related disorders. Precise patient data is vital for hospitals to effectively predict and treat cardiac issues. Utilizing advanced Machine Learning algorithms, such as Logistic Regression, Random Forest, decision trees, K-Nearest Neighbours, Support Vector Machine, Naive Bayes along with statistical models, makes the accurate prediction of heart diseases feasible and accuracy comparison has been made. Finally, a better model has been developed to predict heart disease predictions. Medical practitioners can efficiently detect and address cardiac problems by leveraging these sophisticated algorithms, thereby maximizing the benefits of current medical technology. The application of these algorithms has notably improved patient health and the overall quality of care. Furthermore, hospitals to allocate the resources more precisely perform the predictive analytics more efficiently and enhance patient outcomes. By adopting these technological advancements, medical professionals can stay ahead of potential heart-related issues and deliver better care to their patients. © 2024 IEEE.

关键词： Logistic regression

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SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for AIoT Applications

SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for A...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Xiao, Tao Zhang, Zhenyong Shao, Kuan Li, Hao School of Computer Science and Engineering Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798331507886

As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things (A IoT) systems to run CNN-based applications. However, using CNN-based applications in many AIoT scenarios raises privacy concerns. Protecting the inference phase can preserve the user's privacy, which is an urgent issue according to worldwide rules and policies. In this paper, we propose SeP2CNN, a simple and efficient fully homomorphic encryption (FHE) method to realize the privacy-preserving CNN inference in pervasive AIoT applications. By carefully combining cryptography and deep learning, we compute ReLU with a composite polynomial as the activation function over ciphertexts. To improve the efficiency, we transform the three-dimensional convolution into a two-dimensional convolution to simplify the calculation. Finally, through extensive experiments on multiple datasets, we validate and evaluate the performance of SeP2CNN. © 2024 IEEE.

关键词： Differential privacy

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Knowledge Leaks in data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

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Schmalenbach Journal of Business Research 2024年第3期76卷 357-396页

作者： Fruhwirth, Michael Pammer-Schindler, Viktoria Thalmann, Stefan Know-Center GmbH Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Graz University of Technology Silicon Austria Labs GmbH Business Analytics and Data Science-Center (BANDAS-Center) School of Business Economics and Social Sciences University of Graz

data-driven business models imply the inter-organisational exchange of data or similar value objects. data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. © The Author(s) 2024.

关键词： Business model innovation data analytics data-driven business models Knowledge risks Risk management Value objects

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Transfer Learning for Multiclass Classification of Wheat Leaf Diseases

Transfer Learning for Multiclass Classification of Wheat Lea...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Verma, Riya Singh Rathore, Narendra Pal Sethi, Kamal Kumar Acropolis Institute of Technology and Research Artificial Intelligence and Data Science Indore India Acropolis Institute of Technology and Research Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

In the following paper, we explore a study focusing on the detection of wheat leaf diseases using transfer learning techniques combined with cutting-edge convolutional neural network (CNN) architectures, namely VGG16, ResNet152V2, and Inception. The aim is to enhance the accuracy of disease detection while minimizing computational resources and training time. We conducted experiments on a dataset consisting of wheat leaf images affected with several diseases. Through transfer learning, we fine-tuned pre-trained models on our dataset to leverage their learned features. Our results demonstrate that VGG16 consistently outperforms ResNet152V2 and Inception, achieving the highest accuracy of 98.66% in disease detection. This indicates the efficacy of VGG16 for wheat leaf disease classification tasks, highlighting its potential for agricultural applications requiring robust and accurate detection of plant diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Multiclass Emotion Recognition with CNN-RNN Architecture and Illuminating Module for Real-time Precision using Facial Expressions

Advancing Multiclass Emotion Recognition with CNN-RNN Archit...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Prince, Simrith C. Babu, Nirmal Varghese Computer Science And Engineering Karunya Institute Of Technology And Sciences Coimbatore India Data Science And Cyber Security Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Emotions, intrinsic to the human experience, serve as the catalyst for a groundbreaking technological innovation in multiclass emotion recognition through facial expressions. This study introduces a revolutionary system that harnesses advanced deep learning techniques, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to unravel the intricate interplay between emotions and cutting-edge technology. Commencing with a nuanced exploration of the evolutionary origins of emotions, the research lays a robust foundation for understanding the neural mechanisms behind emotional responses. The proposed algorithm unfolds in two pivotal stages: a meticulously designed CNN extracts intricate features from raw facial images, capturing nuanced expressions, while an adaptive RNN establishes temporal dependencies between sequential frames, allowing for a comprehensive understanding of the dynamic nature of emotional states. To address real world challenges, an innovative illuminating module is seamlessly integrated, accounting for contextual factors such as lighting conditions. Empirical evaluations on benchmark datasets underscore the system's exceptional accuracy, achieving an impressive 87.2% accuracy post hyperparameter tuning, surpassing state-of-the-art methods. This transformative method not only precisely captures static facial features but dynamically interprets evolving emotional expressions in real-time, marking a paradigm shift in the field of emotion recognition. Beyond its technical achievements, the proposed system holds profound implications across psychology, human-computer interaction, and market research, positioning itself as a pioneering force in the evolving landscape of emotion-centric technologies. © 2024 IEEE.

关键词： Emotion Recognition

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Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling 13

Learning Heuristics via Genetic Programming for Multi-Mode R...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tian, Yuan Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

ISBN: (纸本)9798350308365

The multi-mode resource-constrained project scheduling problem (MRCPSP) is a challenging problem for researchers and practitioners in operations research and project management. MRCPSP involves both selecting the execution mode for each activity and sequencing the activities in the schedule. Thus, activity prioritisation and mode selection are the two main decisions in building a project schedule. A rule-based heuristic approach is commonly used for solving this problem in practical complex scenarios. However, designing effective project scheduling rules highly relies on the expertise of professionals and domain knowledge. To address the above issue, this paper proposes a genetic programming-based hyper-heuristic (GPHH) to design heuristic rules automatically. Various decision strategies based on decision orders are proposed and their impact on the capacity of GPHH to learn effective scheduling rules is investigated. The experiment results demonstrate the evolved rules generated by GPHH outperform the existing manual heuristic rules and making two decisions simultaneously is identified as the most effective strategy. © 2024 IEEE.

关键词： Genetic programming

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Revolutionizing Human Activity Recognition in Healthcare: Harnessing Red Deer for Feature Selection and Focal Loss-Based MLP for Classification 32

Revolutionizing Human Activity Recognition in Healthcare: Ha...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Bharot, Nitesh Breslin, John G. Verma, Priyanka Data Science Institute University of Galway Galway Ireland University of Limerick Electronics and Computer Engineering Limerick Ireland

ISBN: (纸本)9789532901382

Internet of Things (IoT) and Artificial Intelligence (AI) advancements have notably improved Human Activity Recognition (HAR) for healthcare, using sensor-based wearables to monitor daily and health activities. However, accuracy challenges arise in detecting specific movements like walking upstairs and walking downstairs due to limited training data and complex behaviours. Traditional HAR methods depend on large, accurately labelled datasets, but practical implementations often suffer from reduced accuracy when test subject data is scarce. Additionally, identifying key features for precise classification remains a critical task. In response, we present the Advanced Human Activity Recognition (AHAR) methodology. AHAR employs a bio-inspired feature selection technique to create an enriched feature-selected dataset. This dataset is then subjected to classification using the Multi-Layered Perceptron (MLP) model with focal loss, which helps focus on weakly labelled data as well. When evaluated on the UCI-HAR dataset, our proposed methodology demonstrates significantly improved recognition accuracy by 2% compared to prevailing state-of-the-art techniques, thus providing a robust and reliant HAR system. © 2024 University of Split, FESB.

关键词： Feature Selection

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