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Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network 3

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

引用

3rd international conference on Computer Applications Technology, CCAT 2024

作者： Jiang, Xin Chen, Zhi-Chao Sun, Zheng Liu, Ming-Zhe Chongqing University of Technology School of Artificial Intelligence Chongqing401135 China Chongqing University Cancer Hospital Department of Radiology Chongqing400030 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu610000 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

ISBN: (纸本)9798331539795

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring. © 2024 IEEE.

关键词： Radiotherapy

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Secure Deep learning Inference with Intel SGX on Intel Ice Lake-SP Xeon Processor 10

Secure Deep Learning Inference with Intel SGX on Intel Ice L...

引用

10th international conference on Smart Computing and Communication, ICSCC 2024

作者： Ling, Phuah Chai Sheikh, Usman Ullah Faculty of Electrical Engineering Universiti Teknologi Malaysia. Malaysia

ISBN: (纸本)9798350363104

Deep learning, being one of the most reputable techniques deployed for artificial intelligence, has been actively used in many applications nowadays including those that are security critical. With more industries now migrated their applications to the cloud or to the edge, it has sparked some serious security concerns. Although securing a deep learning inference has been an area for explorations for many researchers over the years, majority of the efforts revolve around securing the input data and the deep learning model, without much focus on securing the application code or the inference forward pass. Among the most popular methodologies proposed to secure a deep learning inference are cryptographic primitives and trusted hardware. Due to the high performance overhead incurred by cryptographic primitives, this paper proposed to secure a deep learning inference application through the trusted hardware approach, particularly via the Intel SGX on 3rd Gen Intel® Xeon Scalable processor. Through this research, it was discovered that Intel SGX incurred up to around 70% loss in the number of inferences per second and an overhead of up to 13X for the overall application runtime. Nevertheless, this research has demonstrated that with the greatly expanded Intel SGX enclave size on the first Intel Xeon Scalable Processor that comes with Intel SGX support, it is feasible to secure a deep learning application with Intel SGX without any code modification despite the trade-off on the performance. © 2024 IEEE.

关键词： Deep learning

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A Domain-Invariant Feature learning Framework for Histopathology Images 3

A Domain-Invariant Feature Learning Framework for Histopatho...

引用

3rd international conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Fang, Kun Ding, Guangtai College of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9781510668355

In clinical practice, deep learning models commonly encounter performance degradation in actual applications, mainly due to domain shifts caused by heterogeneity from various sources such as scanners, stains, and medical sites. To address this issue, we examine the single-source domain generalization problem - developing a deep network that can withstand unseen domains, using training data from only one source domain. Our solution involves a novel domain-invariant feature learning model, integrating attention normalization and style enhancement modules at shallow layers of the network while exploring the optimal combination strategy in the convolution block. Furthermore, we use color transformation technology to randomly convert images to produce images with different color distributions to eliminate the effects of stains. The experiments demonstrate that our proposed approach achieves state-of-the-art performance in pathological classification tasks and can be extended to unseen domains. © 2023 SPIE.

关键词： Network layers

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An Efficient and Privacy-preserving Federal learning Scheme 3

An Efficient and Privacy-preserving Federal Learning Scheme

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3rd international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Liu, Shaohua Fu, Zhiqiang Department of Management Engineering and Equipment Economics Naval University of Engineering Wuhan China School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9781510671485

With the rapid development of artificial intelligence technologies, federated learning (FL) is increasingly linked to the Internet of Things (IoT). Recently, researchers have found that the screening and privacy of low-quality users has a significant impact on the training of federation learning. The goal of this work is to ensure that data is trained locally, that data is not out of domain, and that the process of model aggregation is performed in ciphertext, while FL models can be generated quickly and iteratively. However, existing work is still in its infancy, with most research efforts focused on the protection of private data, and implementing their solutions is the main challenge. To address this problem, we propose an efficient and privacy-preserving federal learning scheme with the ability to screen users with low computational power. Specifically, we design a new scheme to reduce the impact of excessive time costs caused by low computational power users by iteratively executing our "Low quality user screening algorithm". Meanwhile, to achieve secure aggregation of the model, we invoke a security framework based on the threshold Palile cryptosystem [8]. Experimentally, we prove that our scheme can greatly improve the training efficiency and save the time overhead of training. © 2023 SPIE.

关键词： Internet of things

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Facial Emotional Recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model 3

Facial Emotional Recognition Using Faster Regional Convoluti...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yamsani, Nagendar Jabar, Muhammed Basim Adnan, Myasar Mundher Hussein, A.H.A. Chakraborty, Subhra School of Computer Science and Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317022

Facial Emotion Recognition (FER) poses a significant challenge when training deep learning (DL) models like Artificial Neural Network (ANN) and machine learning (ML). The primary issue in FER is the detection of the face, the creation of bounding boxes, and the classification of emotional expressions. This paper proposes the use of Faster Regional Convolutional Neural Network (RCNN) for facial recognition and employs a median filter to filter the image dataset. The model leverages the Facial Emotional Recognition (FER 2013) dataset for training and predicting accurate classifications. The Median Filter is used to eliminate noise in the image, such as Salt and Pepper noise, and data augmentation is utilized to generate synthetic data for addressing minor imbalances in classes. Subsequently, the VGG 16 architecture is implemented for precise feature extraction to reduce complexity in the classification process. This paper, achieved accuracy, precision, recall, specificity, and f1-score values of approximately 78.22%, 75.40%, 80.20%, 85.90%, and 71.40%, respectively, when compared to other classification methods like Convolutional Neural Network, ResNet 50, Divide-And-Conquer-Based learning, and Resnet 18. © 2023 IEEE.

关键词： Artificial Neural Network Convolutional Neural Network Deep learning Facial Emotion Recognition Median Filter

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Application research of plant leaf pests and diseases base on unsupervised learning 3

Application research of plant leaf pests and diseases base o...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Pei, Mingjing Kong, Min Fu, Maosheng Zhou, Xiancun Li, Zusong Xu, Jieru School of Information Engineering West Anhui University Lu'an China

ISBN: (纸本)9781665459112

In agricultural productivity, detecting plant pests and diseases is extremely crucial. This research studies images of plant leaf pests and diseases from an unsupervised perspective to solve the problem that existing plant leaf disease datasets are difficult to acquire and include few types of diseases, and they cannot find the defective parts of leaves. This paper utilizes the idea of image restoration and uses a deep learning correlation model to detect and localize the abnormal regions of plant leaves. The experimental results show that the img_AUCROC and pixel_AUCROC level anomaly detection and localization achieve good results, which bring influence and reference to other peers. © 2022 IEEE.

关键词： Unsupervised learning

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Research on Scheduling of Multiple Equipment in Flexible Production Line System Based on Tool data Acquisition 3

Research on Scheduling of Multiple Equipment in Flexible Pro...

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3rd international conference on Testing Technology and Automation engineering, TTAE 2023

作者： Qiu, Jianzhong Wu, Jun Chen, Xi Liu, Yanjin Intelligent Manufacturing Division Sichuan Machinery Research and Design Institute Chengdu China Jinan China

ISBN: (纸本)9781510674769

The available cutting time of the tool directly affects the machining efficiency and quality of the workpiece, and research and analysis should be carried out to improve the monitoring accuracy of the available cutting time of the tool. Therefore, this paper studies the improvement methods for the above problems, and proposes a production scheduling decision-making method and system for multiple CNC machining centers that includes the constraints of available cutting time of tools. The visual interface is used to realize the production scheduling management of multiple CNC machining centers. Verify the feasibility of the development system. The test results show that the system developed in this paper can optimize the production scheduling scheme through the allocation of workpiece processing tasks and the selection of processing equipment under the constraint of the available cutting time of the tool, which can effectively improve the utilization efficiency of tools and processing equipment, and improve the quality of the workshop while ensuring the processing quality. © 2024 SPIE.

关键词： data acquisition

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Evaluating Model Performance Through a User-Centric Explainable Framework for Probabilistic Load Forecasting Models 3

Evaluating Model Performance Through a User-Centric Explaina...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Robin, Rebecca Heistrene, Leena Belikov, Juri Baimel, Dmitry Levron, Yoash Nirma University Department of Electrical Engineering Gujarat Ahmedabad India School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Tallinn University of Technology Department of Software Science Tallinn Estonia Sami Shamoon College of Engineering Electrical Electronics Engineering Department Beer-Sheva Israel Technion-Israel Institute of Technology The Andrew and Erna Viterbi Faculty of Ele. Comp. Engg. Haifa Israel

ISBN: (纸本)9798350349207

Load forecasting models ensure efficient, secure, and stable operation of the modern power system. Probabilistic forecasting accounts for uncertainties associated with missing features that are often overlooked by deterministic approaches. However, machine learning-based probabilistic models are complicated. This paper proposes a user-centric explainable AI framework that presents global and local interpretations aligned with the expertise and explanation needs of the targeted user. The overall influence of temporal and spatial exogenous features at the model development stage is evaluated using the Permutation Feature Importance technique. Such an explanation provides a holistic picture of the knowledge gained by the Gradient Boosting Regressor-based probabilistic load forecasting model. Further-more, the proposed framework suggests the implementation of SHapely Additive exPlanations (SHAP) at the post-deployment stage for individual forecast instances. Local explanations provided by SHAP are used to distinguish between interval forecasts with higher and lower forecast accuracy. Such distinction is applied for both the lower and upper bounds of the forecast interval. This is specifically useful for the non-AI expert end-users that need load forecasts for their strategizing their daily operations. This work is validated on the Kaggle data set on the national load demand of Panama supported with several other exogenous features such as weather-related quantities, holidays, and date-time details. Results show the efficacy of the proposed framework and its ability to provide user-friendly interpretations aligned with users' explanation goals. © 2024 IEEE.

关键词： Forecasting

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Instance Selection Techniques for Large Volumes of data 24th

Instance Selection Techniques for Large Volumes of Data

引用

24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Cubillos, Marco Antonio Peña Ballesteros, Antonio Javier Tallón Department of Electronic Computer Systems and Automation Engineering International University of Andalusia Huelva Spain

ISBN: (纸本)9783031482311

Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that involves eliminating irrelevant and superfluous data while maintaining a high level of classification accuracy. IS, as a strategic filtering mechanism, addresses these challenges by retaining essential instances and discarding hindering elements. This refinement process optimizes classification algorithms, enabling them to excel in handling extensive datasets. In this research, IS offers a promising avenue to strengthen the effectiveness of classification in various real-world applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： data mining

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IoT Based intelligent Pill Dispenser for Elderly People 3

IoT Based Intelligent Pill Dispenser for Elderly People

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Deepan, P. Hemavarshini, J.P. Kowsalya, P. Rajasekaran, C. K S Rangsamy College of Technology Department of Electronics and Communication Engineering TamilNadu Tiruchengode India

ISBN: (纸本)9798350322842

In exponentially modernizing world, the growing technologies and lifestyle aids health sectors. In 2013, 13% of the world's population was 60 years of age or older, which has increased from 8% in 1950. Over the next forty years, it will grow more quickly, reaching 21% in 2050. Elderly patients with continuing loss of memory usually forget to take medicines at the right time. Treatment is considered unusable when the patient neglects to take their medication at the appropriate time as directed by their doctor. Therefore, this research presents the implementation of IoT based intelligent pill dispenser for users who should take their medicine at the proper time. The system consists of a solid prescription intelligent pill dispenser and a mobile application for configuring and monitoring it. Added features have been included to the smart medicine box such as high security, emergency alert through SMS, automatic data collection and pill details. The mobile application performs person authorization using a username and password, which is based on the Internet of Things (IoT). It monitors the parameters such as intake of medication time, pills and location details, and user and caretaker data. Additionally, the system notifies users via mobile notifications when it's time to take their medications as well as through the visuals and sounds the dispenser emits. The parameters are recorded, uploaded to the cloud and reviewed by the clinicians using the IoT system. This helps clinicians to gain knowledge about their patient's health conditions for a further treatment analysis. The feature of uploading the patient details and medical records avoids the difficulties of carrying the prescription and medical records and hence patients can live independently. © 2023 IEEE.

关键词： Internet of things

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