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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Introspective learning: a two-stage approach for inference in neural networks 22

Introspective learning: a two-stage approach for inference i...

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proceedings of the 36th international conference on Neural Information Processing Systems

作者： Mohit Prabhushankar Ghassan AlRegib Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (纸本)9781713871088

In this paper, we advocate for two stages in a neural network's decision making process. the first is the existing feed-forward inference framework where patterns in given data are sensed and associated with previously learned patterns. the second stage is a slower reflection stage where we ask the network to reflect on its feed-forward decision by considering and evaluating all available choices. Together, we term the two stages as introspective learning. We use gradients of trained neural networks as a measurement of this reflection. A simple three-layered Multi Layer Perceptron is used as the second stage that predicts based on all extracted gradient features. We perceptually visualize the post-hoc explanations from both stages to provide a visual grounding to introspection. For the application of recognition, we show that an introspective network is 4% more robust and 42% less prone to calibration errors when generalizing to noisy data. We also illustrate the value of introspective networks in downstream tasks that require generalizability and calibration including active learning, out-of-distribution detection, and uncertainty estimation. Finally, we ground the proposed machine introspection to human introspection for the application of image quality assessment.

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Boosting-Based Construction of BDDs for Linear threshold Functions and Its application to Verification of Neural Networks 26th

Boosting-Based Construction of BDDs for Linear Threshold Fun...

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26th international conference on Discovery Science, DS 2023

作者： Tang, Yiping Hatano, Kohei Takimoto, Eiji Kyushu University Fukuoka Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9783031452741

Understanding the characteristics of neural networks is important but difficult due to their complex structures and behaviors. Some previous work proposes to transform neural networks into equivalent Boolean expressions and apply verification techniques for characteristics of interest. this approach is promising since rich results of verification techniques for circuits and other Boolean expressions can be readily applied. the bottleneck is the time complexity of the transformation. More precisely, (i) each neuron of the network, i.e., a linear threshold function, is converted to a Binary Decision Diagram (BDD), and (ii) they are further combined into some final form, such as Boolean circuits. For a linear threshold function with n variables, an existing method takes O(n2n2) time to construct an ordered BDD of size O(2n2) consistent with some variable ordering. However, it is non-trivial to choose a variable ordering producing a small BDD among n! candidates. We propose a method to convert a linear threshold function to a specific form of a BDD based on the boosting approach in the machine learning literature. Our method takes O(2npoly (1 / ρ) ) time and outputs BDD of size O(n2ρ4ln1ρ), where ρ is the margin of some consistent linear threshold function. Our method does not need to search for good variable orderings and produces a smaller expression when the margin of the linear threshold function is large. More precisely, our method is based on our new boosting algorithm, which is of independent interest. We also propose a method to combine them into the final Boolean expression representing the neural network. In our experiments on verification tasks of neural networks, our methods produce smaller final Boolean expressions, on which the verification tasks are done more efficiently. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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Classification and Localization of COVID-19 based on a Pneumonia Radiograph using a Deep learning Approach 22

Classification and Localization of COVID-19 based on a Pneum...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： M. Anto Bennet S. S. Saranya Dinesh Goyal Pankaj Dadheech S. Balu Sudhakar Sengan Electronics & Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology India and Electronics & Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology India School of Computing SRM Institute of Science and Technology Kattankulathur Campus India and School of Computing SRM Institute of Science and Technology Kattankulathur Campus India Computer Science & Engineering Poornima Institute of Engineering and Technology India and Computer Science & Engineering Poornima Institute of Engineering and Technology India Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan (SKIT) India and Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan (SKIT) India Computer Science & Engineering Paavai Engineering College India and Computer Science & Engineering Paavai Engineering College India Computer Science & Engineering PSN College of Engineering and Technology India and Computer Science & Engineering PSN College of Engineering and Technology India

ISBN: (纸本)9781450399937

the world is battling the pandemic corona virus disease (COVID-19) now, and even after 2 years of the COVID-19 pandemic, this technology is still not reasonably advanced to tackle the battle against this virus most efficiently. the total number of COVID-19 cases worldwide surpassed 420 million, with 5.8 million deaths. COVID-19 infects a person with various symptoms, and one of the symptoms is pneumonia. A person suffering from pneumonia may or may not be carrying COVID-19. this research article aims to describe an X-radiation (X-ray) of patients with pneumonia and proposes other subjects who also have the corona virus. this system is based on Deep learning (DL), and the Convolutional Neural Networks (CNN) method is applied. the work is done with additional help from the frameworks Tensorflow and Keras. Firstly, the images are loaded into the compiler, cleaned, and preprocessed accordingly. the next thing to come up with is setting up the Neural Network (NN) layer. the CNN layer is formed, and a particular Activation Function (AF), Rectified Linear Unit (ReLu), is applied. Finally, the model is trained, and a classification is done to determine whether the patient's X-ray is only for pneumonia or pneumonia + COVID-19. the paper's outcome has an accuracy of 96% to 98%.

关键词： X-Rays, Pneumonia, Accuracy, ReLu COVID-19, CNN, Deep learning

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An Integrated Approach of GIS and machine learning to Assess the Spatio-Temporal Earthquake Vulnerability in South Africa 4

An Integrated Approach of GIS and Machine Learning to Assess...

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4th IEEE international conference on Computing and Information Sciences, ICCIS 2021

作者： Atif, Iqra Cawood, Frederick thomas Mahboob, Muhammad Ahsan Ali, Sarfraz University of the Witwatersrand Wits School of Geography Archaeology and Environmental Studies Johennesburg South Africa University of the Witwatersrand Johennesburg South Africa

ISBN: (纸本)9781665494410

South Africa is experiencing a high frequency of seismic events which have catastrophic effects on individuals and infrastructure. this study aims to utilize the integrated approach of Geographical Information Systems (GIS) and machine learning (ML) based algorithms to analyse the large amount of seismicity data to discover the meaningful patterns and assess the geo-vulnerability of earthquakes with good confidence level in South Africa. Several analytical and modelling techniques including Space-Time Pattern Mining, Artificial Neural Networks (ANN) based hot and cold spots were applied. the results of earthquake data from year 1973 to 2021 revealed that in total there were 1, 680 earthquake events that occurred with magnitude ranges from mild to moderate (2 ≤ M ≤ 5). Earthquakes with higher magnitude were concentrated notably in the Gauteng (48%) followed by North-West (31%) provinces of the South Africa. Also, 63% of the magnitude and depth of earthquakes are oriented from North-East to South-West direction. A significant increasing trend of earthquake was observed in some areas of Free State (p ≤ 0.1), Limpopo (p ≤ 0.1), Western Cape (p ≤ 0.5) and Gauteng (p ≤ 0.5) provinces. Whereas decreasing trend was found in areas of North-West (p ≤ 0.1) and Mpumalanga (p ≤ 0.5). the ANN based hot spot analysis predicted the cluster of high magnitude earthquakes (hot spots) in North-West province and low magnitude earthquakes (cold spots) in Gauteng province. Although the earthquake vulnerability is low in Gauteng province but these cold spots could be related to the deep mining activities in the region and have the potential to trigger the rock burst phenomena at the mines. the results can help the disaster management authorities for smart decision making, and urban and regional planning of future activities in the region. © 2021 IEEE.

关键词： Neural networks

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Deep-learning algorithm for rib fracture detection in low-dose chest CT images 22

Deep-learning algorithm for rib fracture detection in low-do...

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proceedings of the 2022 9th international conference on Biomedical and Bioinformatics Engineering

作者： Hui Yu Yongzheng Ding Jinglai Sun Xuyao Yu Fengling Zhu Huanming Li Xiaoyun Liang Dagong Jia Key Laboratory of Biomedical Detecting Techniques and Instruments Tianjin University China Tianjin Medical University Cancer Institute & Hospital China Tianjin Hospital China Tianjin Joint Laboratory of intelligent medicine Tianjin 4TH Centre Hospital China School of Precision Instruments and Optoelectronics Engineering Tianjin University China

ISBN: (纸本)9781450397223

the detection of rib fractures is of great significance for the diagnosis and treatment of patients with blunt chest trauma. the while application of low-dose CT has become a trend. But there are few studies focusing on automatic fracture detection under low-dose CT. In this paper, a deep-learning model was proposed based on Mask R-CNN and its performance was evaluated by public dataset RibFrac containing 480 chest CT images with 3D rib fracture annotations. In addition, another dataset including 100 cases were collected from Tianjin Hospital for extensibility *** curve was used to compare the effects of different doses of CT images while the precision, recall, F1 score, IoU index and Dice coefficient were calculated. On the rib detection task,the detection rates achieved 93.5%, 90.8%, and 90.5% at normal dose, 10% mAs, and 5% mAs, respectively. On the external dataset,the recall rates of detection achieved of 95.7%, 93.9% and 92.7%.Our proposed network can effectively realize the task of detecting rib fractures in low-dose CT, and provide help for the application of deep learning for rib fracture detection in low-dose CT .

关键词： Object detection Low-dose CT Rib fracture Deep learning

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De novo Drug Design against SARS-CoV-2 Protein Targets using SMILES-based Deep Reinforcement learning 23

De novo Drug Design against SARS-CoV-2 Protein Targets using...

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proceedings of the 2023 11th international conference on Information Technology: IoT and Smart City

作者： Xiuyuan Hu Yanghepu Li Guoqing Liu Yang Zhao Hao Zhang Liang Zhao Department of Electronic Engineering Tsinghua University China Graduate School of Advanced Integrated Studies in Human Survivability Kyoto University Japan AI4Science Microsoft Research China

ISBN: (纸本)9798400709043

De novo drug design is an important task within the field of computer-aided drug design, and in recent years, numerous machine learning algorithms have been proposed for this purpose. the SARS-CoV-2 virus has posed a severe crisis to humanity over the past few years, making drug design targeting its protein targets a critical challenge. In this paper, we introduce a SMILES-based deep reinforcement learning algorithm to design small molecule inhibitors that bind well with SARS-CoV-2 targets. Experimental results demonstrate that our algorithm is capable of generating satisfactory drug candidates against SARS-CoV-2 protein targets and has the potential to be extended to other targets.

关键词： SARS-CoV-2 de novo drug design molecular generation reinforcement learning

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application of integrated energy data processing based on real-time stream processing platform 23

Application of integrated energy data processing based on re...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Xinyuan Jiao Zheng Hou Mimi Zhang Dechao Feng Zhencong Cai State Grid Information and Telecommunication Group Co. Ltd. China Beijing Energy Group Co. Ltd. China

ISBN: (纸本)9798400709517

Integrated energy is an important component of the new power system, and its IoT components need to collect and process energy equipment measurement data in real time. In order to solve the problems of large batches of equipment measurement data, measurement data requiring multiple calculations based on the relationship between equipment, and timely data processing requirements, distributed real-time data synchronization and processing technology is introduced to build a real-time stream processing platform, thereby ensuring stable operation of the system and efficient use of computing resources. First, the key technologies of the real-time stream processing platform are introduced, then the design of the integrated energy data collection and processing architecture is explained, and finally the real-time stream processing platform is built and functionally verified.

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application research of substation management based on BIM technology 23

Application research of substation management based on BIM t...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Baitao Zhang Kaixin Gu Yi Yang Zhenzhe Zhang Haojie Ren State Grid Jiangsu Electric Power Engineering Consulting Co. Ltd. China East China Electric Power Design Institute Co. Ltd. Of China Power Engineering Consulting Group China

ISBN: (纸本)9798400709517

the development of BIM technology in the domestic engineering field, the most important thing at present is to pay close attention to the implementation of BIM landing application, closely in line with industrial changes and development trends. In the process of substation construction management, construction quality management and cost control are affected by many factors, and the importance of these factors is different, so the concept of prefabricated in the construction industry is introduced. In the past, WBS principle of work breakdown structure has been used for quality control. therefore, the construction and development of power transmission and transformation projects need to be integrated with the application progress of BIM technology and coding system to accelerate the application of three-dimensional design results. In the field of model coding system, this paper proposes a new algorithm for digital 3D model identification system, which has achieved initial results in practical engineering cases. Finally, the paper analyses the key difficulties and development direction of BIM technology application in substation construction in the future.

关键词： BIM application Construction management Model coding Substation three-dimensional building information model

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Enhanced Classification of Intel Images Using Refined EfficientNet and MobileNetV2 Frameworks

Enhanced Classification of Intel Images Using Refined Effici...

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Intelligent Technologies (CONIT), international conference on

作者： Raghav Jain Rishabh Sharma Devesh Tiwari Kireet Joshi Vishal Jain Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida U.P India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the proposed study describes the functioning of two modern deep learning models, namely EfficientNet and MobileNetV2, within the scope of image classification from a wide range of datasets. this study is designed to categorize images into six groups, namely: buildings, mountains, forests, sea/ocean, glaciers, and streets. the study utilizes a dataset from the Kaggle repository to assess the effectiveness of these models. the research methodology will include data preprocessing, modeling, and model evaluation. this paper will find out the balance between accuracy and efficiency which is provided by each model. EfficientNet is compared with MobileNetV2 because of its scalable structure and compromise between model depth, width, and resolution versus the latter which is known for its low weight and efficiency, especially in constrained circumstances. the research utilizes a framework of accuracy, precision, recall, F1-score metrics, and model efficiency analyses for comprehensive result evaluation and scrutiny. the analysis of this study indicates the unique advantages of separate models, for instance, EfficientNet is more accurate in most cases and MobileNetV2 is a good solution when you have to save computer resources. Comparing models highlights the importance of the selection that is made based on the specific application instead of performance metrics or computational demands.

关键词： Deep learning Training Analytical models Accuracy Forests Computational modeling Glaciers

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Superpixel Clustering and Planar Fit Segmentation of 3D LIDAR Point Clouds

Superpixel Clustering and Planar Fit Segmentation of 3D LIDA...

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international conference on Computing for Geospatial Research and application

作者： Hamid Mahmoudabadi Timothy Shoaf Michael J. Olsen School of Civil and Construction Engineering Oregon State University Department of Electrical Engineering and Computer Science Oregon State University

ISBN: (纸本)9781479903016

Terrestrial laser scanning (TLS, also called ground-based Light Detection and Ranging, LIDAR) is an effective data acquisition method capable of high precision, detailed 3D models for surveying natural environments. However, despite the high density, and quality, of the data itself, the data acquired contains no direct intelligence necessary for further modeling and analysis - merely the 3D geometry (XYZ), 3-component color (RGB), and laser return signal strength (I) for each point. One common task for LIDAR data processing is the selection of an appropriate methodology for the extraction of geometric features from the irregularly distributed point clouds. Such recognition schemes must accomplish both segmentation and classification. Planar (or other geometrically primitive) feature extraction is a common method for point cloud segmentation;however, current algorithms are computationally expensive and often do not utilize color or intensity information. In this paper we present an efficient algorithm, that takes advantage of both colorimetric and geometric data as input and consists of three principal steps to accomplish a more flexible form of feature extraction. First, we employ a Simple Linear Iterative Clustering (SLIC) superpixel algorithm for clustering and dividing the colorimetric data. Second, we use a plane-fitting technique on each significantly smaller cluster to produce a set of normal vectors corresponding to each superpixel. Last, we utilize a Least Squares Multi-class Support Vector machine (LSMSVM) to classify each cluster as either "ground", "wall", or "natural feature". Despite the challenging problems presented by the occlusion of features during data acquisition, our method effectively generates accurate (>85%) segmentation results by utilizing the color space information, in addition to the standard geometry, during segmentation.

关键词： LIDAR Laser Point Cloud Superpixel machine learning Clustering SVM

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