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共 2655 条记录，以下是121-130 订阅

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Application of Computer Vision Based on Deep learning in Automatic recognition of Pathological Images 6

Application of Computer Vision Based on Deep Learning in Aut...

引用

6th IEEE international conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Huang, Li Zhou, Xiaobiao Yang, Yuanfang Puai Medical College Shaoyang University Hunan Shaoyang China The Central Hospital of Shaoyang Pathology Department Hunan Shaoyang China The Central Hospital of Shaoyang Neurosurgery Department Hunan Shaoyang China

ISBN: (纸本)9798350374315

this paper explores the application potential of deep learning-based computer vision technology in automating pathological image recognition, addressing significant challenges in the field. Deep learning, a powerful machine learning method, extracts high-level abstractions from complex, high-dimensional image data through layers of nonlinear processing units. the study reviews fundamental principles of computer vision and deep learning, analyzing various deep learning models like CNN sand RNN s, and their adaptations for tasks including pathological image classification, target detection, cell segmentation, and tissue structure analysis. Emphasis is placed on an innovative architecture combining attention mechanisms and multi-scale feature fusion to enhance accuracy in detecting subtle pathological changes. Experiments conducted on a public dataset validate the method's efficacy in identifying different cancer types and distinguishing between benign and malignant lesions. Results demonstrate that deep learning models outperform traditional methods, improving diagnostic accuracy while reducing dependency on expert pathologists. Challenges such as high data annotation costs, model interpretability issues, and computational demands are discussed alongside proposed solutions and future research directions. In conclusion, this study underscores the pivotal role of deep learning-based computer vision in advancing automation and intelligence in pathology, offering new technical foundations for enhancing disease diagnosis efficiency and accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Dynamic Black-box Backdoor Attacks on IoT Sensory data 6

Dynamic Black-box Backdoor Attacks on IoT Sensory Data

引用

6th IEEE international conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Chathoth, Ajesh Koyatan Lee, Stephen University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9798350386745

Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition (HAR). Modern wearable and smart devices feature various built-in Inertial Measurement Unit (IMU) sensors, and such sensor-based measurements can be fed to a machine learning-based model to train and classify human activities. While deep learning-based models have proven successful in classifying human activity and gestures, they pose various security risks. In our paper, we discuss a novel dynamic trigger-generation technique for performing black-box adversarial attacks on sensor data-based IoT systems. Our empirical analysis shows that the attack is successful on various datasets and classifier models with minimal perturbation on the input data. We also provide a detailed comparative analysis of performance and stealthiness to various other poisoning techniques found in backdoor attacks. We also discuss some adversarial defense mechanisms and their impact on the effectiveness of our trigger-generation technique. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Review of the Artificial Intelligence (AI) shift in E-commerce 6

A Comprehensive Review of the Artificial Intelligence (AI) s...

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Saleem, Shah Asif Mohd Naseem, Shaikh Mohammad Bilal Somaiya Vidyavihar University Department of Information Technology Mumbai India Somaiya Vidyavihar University Dept of Computer Science/IT Mumbai India

ISBN: (纸本)9798350359817

Artificial Intelligence is really taking over the world. Use of AI is everywhere now, the aim of this research paper is to show how artificial intelligence works and how it's getting involved and solving complex problems in modern world. Specific applications of AI include natural language processing, machine vision, speech recognition, and expert systems. E-Commerce is now adopting this technology to fetch the behavior of their customers based on activity they do in the internet world, like surfing, browsing, as well what they do on social media, and what kind of product they like to see on their feed or show interest in selective products. Many e-commerce giants like Amazon. Flipkart, Myntra, Ajio have already started implementing & observing multiple approaches of AI such as data analysis, deep learning, and pattern recognition to better understand their customers, and provide an enhanced customer experience for each and every one. Personalized shopping using AI involves leveraging customer data and machine learning algorithms to create detailed customer profiles and analyze their preferences and behavior. Artificial intelligence is the next level thing as well as a good use of technology in the modern world. It comes with some drawbacks as well. We have a few things to look at for AI in terms of drawbacks of the same. © 2023 IEEE.

关键词： Deep learning

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Neuro-Cognitive pattern recognition: Advancements in Memory-Related Disorders Identification 6

Neuro-Cognitive Pattern Recognition: Advancements in Memory-...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Vifert Jenuben Daniel, V. Archanaa, N. Faheem, Mohammed Kousihik, K. Suwin Kumar, J.D.T. Muthulakshmi, M. Amrita School of Engineering Amrita Vishwa Vidhyapeetham Department of Artificial Intelligence Chennai India Amrita School of Engineering Amrita Vishwa Vidhyapeetham Department of ECE Chennai India

ISBN: (纸本)9798350306613

the application of machine learning in medical imaging and diagnostics opens up new pathways for disease identification and classification. Our research investigates the use of various machine learning models for Alzheimer's disease detection and classification using MRI image data, including Support Vector machines (SVM) with different kernels, Linear Discriminant Analysis (LDA), and the advanced convolutional neural network, EfficientNetB0. the comparative study is an important component of this research, concentrating not only on the models' performance but also on the theoretical assumptions that underpin their efficacy. this investigation sheds light on why certain models outperform others, notably in the context of Alzheimer's disease classification. the research emphasizes the robust performance of linear models such as SVM and LDA, as well as the complexities of convolutional neural networks. Notably, our findings show a tremendous success with a 98.8 % accuracy rate, highlighting the potential of machine learning in improving diagnostic processes. this study contributes to the expanding field of machine learning in medical diagnostics by providing in-depth analysis and theoretical understanding that guide model selection and optimization in high-stakes healthcare applications. © 2024 IEEE.

关键词： Alzheimer disease CNN EfficientNetB0 LDA MRI PCA SVM

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Free Space and Lane Boundary Fault recognition and Prediction for Independent Vehicles Using machine learning 6

Free Space and Lane Boundary Fault Recognition and Predictio...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Dangi, Sumedha Kumar, Deepak Amity Institute of Information Technology Amity University Noida India

ISBN: (纸本)9798350346961

Autonomous Vehicles research proceeds acceleration as a result of the enormous benefit of an autonomous system. Along with the positivity of self-driving, there are various challenges such as the occurrence of faults, and bugs. therefore, fault detection and prediction is a crucial step for the safety of autonomous vehicles, it can be achieved with the help of machine learning in artificial neural networks. this research proposes a machine learning solution to improve the accuracy and reliability of identifying free space and lane borders in autonomous vehicles. the use of sensor data from cameras and lidars trained machine learning algorithms to recognize and predict driving-related problems. the results showed increased accuracy and resilience compared to traditional computer vision methods, highlighting the potential of machine learning in enhancing the perceptual abilities of autonomous vehicles. the study contributes to the development of safe and reliable autonomous driving systems.. © 2023 IEEE.

关键词： Autonomous vehicles

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Navigating Urban Traffic patterns: Ensemble Stacking for Improved Travel Time Prediction 6

Navigating Urban Traffic Patterns: Ensemble Stacking for Imp...

引用

6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Benlecheb, Amira Chaouche, Ahmed-Chawki Benabderrahmane, Brahim University of Constantine 2 - Abdelhamid Mehri Misc Laboratory Constantine25000 Algeria

ISBN: (纸本)9798350350265

Efficient travel time prediction is crucial for effective transportation planning, traffic management, and urban mobility. In this study, we present a novel approach to enhance travel time prediction accuracy in urban environments using ensemble stacking techniques. Leveraging gradient boosting algorithms as base models, our methodology integrates diverse predictive models to capture complex traffic patterns and improve generalization performance. We conduct empirical evaluations using real-world data to demonstrate the effectiveness of our approach, highlighting its superiority over individual models. Our findings underscore the potential of ensemble stacking in addressing the challenges of travel time prediction in urban settings, paving the way for more reliable and efficient transportation systems. © 2024 IEEE.

关键词： machine learning

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Dynamic data Augmentation with Gating Networks for Time Series recognition 26

Dynamic Data Augmentation with Gating Networks for Time Seri...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Oba, Daisuke Matsuo, Shinnosuke Iwana, Brian Kenji Kyushu Univ Dept Adv Informat Technol Fukuoka Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method is not equally effective for every dataset, you need to select an appropriate method carefully. We propose a neural network that dynamically selects the best combination of data augmentation methods using a Gating Network and a mutually beneficial feature consistency loss. the Gating Network is able to control how much of each data augmentation is used for the representation within the network. the feature consistency loss gives a constraint that augmented features from the same input pattern should be in similar. In the experiments, we demonstrate the effectiveness of the proposed method on the 12 largest time-series datasets from 2018 UCR Time Series Archive and reveal the relationships between the data augmentation methods through analysis of the proposed method.

关键词： Histograms Analytical models Time series analysis Neural networks Radar machine learning pattern recognition

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the Predictive Modelling of Student Academic Performance Using machine learning Approaches 6th

The Predictive Modelling of Student Academic Performance Usi...

引用

6th international conference on Soft Computing and data mining (SCDM)

作者： Rahman, Nurul Habibah Abdul Sulaiman, Sahimel Azwal Ramli, Nor Azuana Univ Islam Pahang Sultan Ahmad Shah Dept Business Fac Management & Informat Jalan Gambang Kuantan 25150 Pahang Malaysia Univ Malaysia Pahang Ctr Math Sci Kuantan Pahang 26300 Malaysia

ISBN: (纸本)9783031669644;9783031669651

In the field of education, machine learning techniques have been applied in numerous studies covering a wide range of topics, including student enrollment, graduation forecasts, failure rates, retention, and academic performance. the use of predictive analytics in machine learning offers valuable insights to educators, potentially aiding in the improvement of students' outcomes through the analysis of historical data. However, based on a review of existing literature, research focusing on the use of machine learning and predictive analytics to enhance student performance in Malaysian higher education remains limited, specifically among Islamic Universities. the primary objective of this study is to create the most effective predictive model for forecasting students' final grades by employing machine learning methods such as Multinomial Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, and Support Vector machine. this research utilizes a dataset comprising student records from the Business Statistics course at Universiti Islam Pahang Sultan Ahmad Shah, spanning from 2013 to 2022. the findings reveal that the Decision Tree model is the most accurate, with a 0.60 accuracy rate in predicting students' performance levels. this optimal model is instrumental in enabling lecturers to identify students at risk of failing at an early stage.

关键词： machine learning Predictive models Students' performance Education

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Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption 26

Discovery of New Multi-Level Features for Domain Generalizat...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Frikha, Ahmed Krompass, Denis Tresp, Volker Siemens Technol Mumbai Maharashtra India Univ Munich LMU Munich Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain generalization (DG) problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without any exposure to their data. the main challenge of DG is that the features learned from the source domains are not necessarily present in the unseen target domains, leading to performance deterioration. We assume that learning a richer set of features is crucial to improve the transfer to a wider set of unknown domains. For this reason, we propose COLUMBUS, a method that enforces new feature discovery via a targeted corruption of the most relevant input and multi-level representations of the data. We conduct an extensive empirical evaluation to demonstrate the effectiveness of the proposed approach which achieves new state-of-the-art results by outperforming 18 DG algorithms on multiple DG benchmark datasets in the DOMAINBED framework.

关键词： Training machine learning Benchmark testing data models pattern recognition

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A Multi-end Application System for Animal Disease Prevention and Control Based on Spark and Deep learning 6

A Multi-end Application System for Animal Disease Prevention...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Guihua, Zhang Hui, Zhang Yuanyuan, Huang Chengdu Jincheng College Department of Computer and Software Sichuan China

ISBN: (纸本)9798350325485

With the increasing production scale and breeding density of poultry and animal husbandry in China, the source of animal diseases has become complicated, and how to effectively carry out epidemic prevention and control has become more and more urgent. In this paper, big data technology and deep learning algorithm are applied to epidemic prevention and management, providing possibilities for the early identification and diagnosis, early warning and trend assessment of current animal diseases. Taking poultry as an example, convolutional neural network algorithm was used to identify chicken manure images, classify the most common poultry diseases, improve the traditional chicken disease detection methods, and improve the accuracy and speed of chicken disease detection. At the same time, in the face of the huge multi-source data information in the monitored area, distributed technology is adopted to manage and store data, build data centers and data warehouses, and connect to the Spark big data computing platform for analysis, calculation and mining, which improves the data computing speed and utilization rate. On the business application platform, we designed a large screen visualization system on the APP and WEB side and a background management system to realize multi-Terminal applications and information interconnection, multi-dimensional, dynamic and real-Time visualization of animal disease distribution and development situation, improve the comprehensive decision-making ability and macro management ability of functional departments, and realize early identification and early warning of poultry diseases. the efficiency of animal disease prevention and control was improved, and the economic loss of farmers was reduced. © 2023 IEEE.

关键词： Big data

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