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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

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Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

arXiv

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arXiv 2022年

作者： Talukder, Md Alamin Islam, Md Manowarul Uddin, Md Ashraf Akhter, Arnisha Hasan, Khondokar Fida Moni, Mohammad Ali Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Cyber Security Research & Innovation RMIT University 124 La Trobe Street MelbourneVIC3000 Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers. © 2022, CC BY.

关键词： Feature extraction

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Bridging explicit and implicit deep generative models via neural stein estimators 21

Bridging explicit and implicit deep generative models via ne...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Qitian Wu Rui Gao Hongyuan Zha Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University University of Texas at Austin Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University and School of Data Science Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen

ISBN: (纸本)9781713845393

There are two types of deep generative models: explicit and implicit. The former defines an explicit density form that allows likelihood inference; while the latter targets a flexible transformation from random noise to generated samples. While the two classes of generative models have shown great power in many applications, both of them, when used alone, suffer from respective limitations and drawbacks. To take full advantages of both models and enable mutual compensation, we propose a novel joint training framework that bridges an explicit (unnormalized) density estimator and an implicit sample generator via Stein discrepancy. We show that our method 1) induces novel mutual regularization via kernel Sobolev norm penalization and Moreau-Yosida regularization, and 2) stabilizes the training dynamics. Empirically, we demonstrate that proposed method can facilitate the density estimator to more accurately identify data modes and guide the generator to output higher-quality samples, comparing with training a single counterpart. The new approach also shows promising results when the training samples are contaminated or limited.

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An Effective Sensor Filtering Model for Mobile Devices Based on Long Short-Term Memory Technique in Sensor Archives

An Effective Sensor Filtering Model for Mobile Devices Based...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： A. Jasmine Gilda Suresh G S. Sivamrugan Suresh Kumar. R Sushmitha. K R. Thandaiah Prabu Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Chennai

It is expected that the Internet of Things (IoT) would be capable to deliver smart applications by aggregating data through many detectors spread across the natural world. A sensor registration system (SRS) is used by a mobile device in order to provide information obtained from ambient sensors. After this, the mobile device connects directly to get useful data. The Sensor Reporting System (SRS) has to offer information for sensors that have a chance of successfully connecting. This procedure is also known as sensor filtering, and it is dependent on the location of the user. In practice, the GPS may sometimes display an incorrect location, which results in anunable to connect. To improve the completion rate of interactions among mobile devices and environmental detectors, we provide a double collaboration approach that simultaneously collects GPS data and predictions from prior trajectories. Furthermore, we update the procedure for assessing detector filtering in SRS by integrating a Monte Carlo-based modeling stream within the procedure of gauging the resource supply rate. Realistic research shows that LSTM-based path forecasting can efficiently remove position irregularities and make up for the lack of position information.

关键词： Monte Carlo methods Filtering Detectors Predictive models Knowledge discovery Mobile handsets Trajectory

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Exploring the Benefits of DNS-Based Authentication for Wireless Network Security

Exploring the Benefits of DNS-Based Authentication for Wirel...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Lakshya Swarup R. Jayadurga Monali R. Borade Ramachandran T Akhilendra Pratap Singh Savita Prabha Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Wi-wireless networks are becoming more pervasive and are frequently the primary goal of malicious attackers. Therefore, it is miles crucial that wireless networks are nicely secured to defend their users from malicious moves. DNS-based total authentication is a promising answer for attaining ease of authentication of wi-wireless customers in an open Wi-Fi community. This paper surveys numerous current works that discover the wi-fits wireless of DNS-based total authentication for Wi-Fi wireless community safety. especially this paper opinions three one-of-a-kindwireless strategies of using DNS for authentication purposes: making use of DNS's cache for identifying windows-primarily based Wi-Fi wireless clients; Open Mobile Alliance (OMA)'s tool control (DM) projects to relaxed get right of entry to; and a modern scheme for protecting the use of the DNS-based techniques. Wi-finally, this paper discusses a few safety-demanding situations and future studies directions.

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Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs

arXiv

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arXiv 2023年

作者： Neshat, Mehdi Ahmed, Muktar Askari, Hossein Thilakaratne, Menasha Mirjalili, Seyedali Center for Artificial Intelligence Research and Optimisation Torrens University Australia BrisbaneQLD4006 Australia University of South Australia AdelaideSA5000 Australia Australian Centre for Precision Health The University of South Australia Cancer Research Institute AdelaideSA5000 Australia School of Electrical Engineering and Computer Science University of Queensland St Lucia BrisbaneQLD4072 Australia School of Computer Science The University of Adelaide Adelaide5005 Australia

Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately affect people of all ages. As highlighted by the World Health Organization (WHO), this prevalent disease tragically accounts for a substantial 15% of global mortality in children under five years of age. This article presents a comparative study of the Inception-ResNet deep learning model's performance in diagnosing pneumonia from chest radiographs. The study leverages Mendeley's chest X-ray images dataset, which contains 5856 2D images, including both Viral and Bacterial Pneumonia X-ray images. The Inception-ResNet model is compared with seven other state-of-the-art convolutional neural networks (CNNs), and the experimental results demonstrate the Inception-ResNet model's superiority in extracting essential features and saving computation runtime. Furthermore, we examine the impact of transfer learning with fine-tuning in improving the performance of deep convolutional models. This study provides valuable insights into using deep learning models for pneumonia diagnosis and highlights the potential of the Inception-ResNet model in this field. In classification accuracy, Inception-ResNet-V2 showed superior performance compared to other models, including ResNet152V2, MobileNet-V3 (Large and Small), EfficientNetV2 (Large and Small), InceptionV3, and NASNet-Mobile, with substantial margins. It outperformed them by 2.6%, 6.5%, 7.1%, 13%, 16.1%, 3.9%, and 1.6%, respectively, demonstrating its significant advantage in accurate classification. © 2023, CC BY-NC-SA.

关键词： Deep learning

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Gray Learning from Non-IID data with Out-of-distribution Samples

arXiv

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arXiv 2022年

作者： Zhao, Zhilin Cao, Longbing Wang, Chang-Dong The Data Science Lab School of Computing and DataX Research Centre Macquarie University SydneyNSW2109 Australia The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed Gray Learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach

arXiv

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arXiv 2023年

作者： Xiao, Yong Xia, Rong Li, Yingyu Shi, Guangming Nguyen, Diep N. Hoang, Dinh Thai Niyato, Dusit Krunz, Marwan The School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The School of Mech. Eng. and Elect. Inform. The China University of Geosciences Wuhan430074 China The School of Artificial Intelligence The Xidian University Shanxi Xi'An710071 China The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore The Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over 20% improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Machine Learning Ensemble Techniques for Prediction of Diabetes

Machine Learning Ensemble Techniques for Prediction of Diabe...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Toh Kar Ming Samuel-Soma M. Ajibade Muhammed Basheer Jasser Anwar P.P. Abdul Majeed Kehinde Ehineni Verena Atud Achick-Muyu Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Industrial Business Unit Littelfuse The Galleria Saskatoon Canada Wolfson Institute of Population Health Queen Mary University of London London UK

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

This study aims to explore hybrid machine learning techniques for the predictive purpose of diabetes disease. The primary objective is to analyze existing supervised machine learning algorithms like K-Nearest Neighbours (KNN), Naive Bayes (NB), Decision Tree (DT) and so on, that are used for predictive purposes by carrying out extensive research. Through this study, several hybrid machine learning prediction classifiers using supervised techniques and ensemble methods will be studied and evaluated based on their performance matrix of accuracy, precision, recall and F1-score as well. This is to discover which predictive technique is the most suitable for predicting diabetes as well as to evaluate by how much ensemble methods can improve diabetes prediction accuracy. This study utilizes feature selection with Recursive Feature Elimination with Cross Validation (RFECV) to determine the most important features of the dataset, as well as K-Fold Cross Validation to evaluate the performance of all the machine learning techniques used by ensuring that different portions of the dataset is used for training and testing. The results of this study reveal that boosting, by Gradient Boosting (GB), is the best performing ensemble technique at 85.904% accuracy, 70.921 %) precision, 56.840% recall and 58.439% F1-score. For bagging ensemble techniques, Random Forest (RF) had the highest performance at 85.903% accuracy, 71.019% precision, 56.486% recall and 57.939% F1-score. On the other hand, for stacking ensemble techniques, Decision Tree (DT) + Support Vector Machine (SVM) proved to be the highest performing combination with an accuracy of 85.870%, precision of 70.585%, recall of 57.524% and F1-score of 59.358%. For the base supervised techniques itself, K-Nearest Neighbours (KNN) performed almost as well as GB, with the same accuracy as GB (85.904 %), and got a higher precision of 71.050% but had a recall and F1-score that's marginally lower at 56.380% and 57.789% respectively. Th

关键词： Support vector machines Surveys Accuracy Machine learning algorithms Machine learning Nearest neighbor methods Boosting Diabetes Ensemble learning Tuning

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Understanding Deep Learning via Decision Boundary

arXiv

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arXiv 2022年

作者： Lei, Shiye He, Fengxiang Yuan, Yancheng Tao, Dacheng The Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney Australia Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh United Kingdom Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

This paper discovers that the neural network with lower decision boundary (DB) variability has better generalizability. Two new notions, algorithm DB variability and (ϵ, η)-data DB variability, are proposed to measure the decision boundary variability from the algorithm and data perspectives. Extensive experiments show significant negative correlations between the decision boundary variability and the generalizability. From the theoretical view, two lower bounds based on algorithm DB variability are proposed and do not explicitly depend on the sample size. We also prove an upper bound of order (Equation presented) based on data DB variability. The bound is convenient to estimate without the requirement of labels, and does not explicitly depend on the network size which is usually prohibitively large in deep learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision theory

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