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A machine learning based heuristic to predict the efficacy of online sale

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arXiv 2020年

作者： Singhania, Aditya Vikram Mukherjee, Saronyo Lal Majumdar, Ritajit Mehta, Akash Banerjee, Priyanka Bhoumik, Debasmita Department of Computer Science Bhawanipur Education Society College Advanced Computing & Microelectronics Unit Indian Statistical Institute India

It is difficult to decide upon the efficacy of an online sale simply from the discount offered on commodities. Different features have different influence on the price of a product which must be taken into consideration when determining the significance of a discount. In this paper we have proposed a machine learning based heuristic to quantify the "significance" of the discount offered on any commodity. Our proposed technique can quantify the significance of the discount based on features and the original price, and hence can guide a buyer during a sale season by predicting the efficacy of the sale. We have applied this technique on the Flipkart Summer Sale dataset using Support Vector Machine, which predicts the efficacy of the sale with an accuracy of 91.11%. Our result shows that very few mobile phones have a significant discount during the Flipkart Summer Sale. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic commerce

Dynamic Management of Traffic Signals through Social IoT 3

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Dynamic Management of Traffic Signals through Social IoT

3rd International Conference on computing and Network Communications, CoCoNet 2019

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. IoT Lab University Visvesvaraya College of Engineering Bangalore University Bengaluru India Lab School of Computing and Information Systems University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University United States National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru India

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other. © 2020 The Authors. Published by Elsevier B.V.

关键词： Traffic congestion

Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA

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arXiv 2024年

作者： Shaojie, Tang Penpen, Miao Xingyu, Gao Yu, Zhong Dantong, Zhu Haixing, Wen Zhihui, Xu Qiuyue, Wei Hongping, Yao Xin, Huang Rui, Gao Chen, Zhao Weihua, Zhou School of Automation Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Xi'an Key Laboratory of Advanced Control and Intelligent Process Shaanxi Xi’an710121 China Second Clinical Medical School of Nanjing Medical University Jiangsu Nanjing210011 China Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing210000 China Department of Pharmacy The First Affiliated Hospital of Xi’an Jiaotong University Shaanxi Xi’an710061 China Department of Cardiology The First Affiliated Hospital of Xi’an Jiaotong University Shaanxi Xi’an710061 China Department of Nuclear Medicine The First Affiliated Hospital of Xi’an Jiaotong University Shaanxi Xi’an710061 China Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Applied Computing Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

A method was proposed for the point cloud-based registration and image fusion between cardiac single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) and cardiac computed tomography angiograms (CTA). Firstly, the left ventricle (LV) epicardial regions (LVERs) in SPECT and CTA images were segmented by using different U-Net neural networks trained to generate the point clouds of the LV epicardial contours (LVECs). Secondly, according to the characteristics of cardiac anatomy, the special points of anterior and posterior interventricular grooves (APIGs) were manually marked in both SPECT and CTA image volumes. Thirdly, we developed an in-house program for coarsely registering the special points of APIGs to ensure a correct cardiac orientation alignment between SPECT and CTA images. Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images. Finally, the image fusion between SPECT and CTA was realized after the fine registration. The experimental results showed that the cardiac orientation was aligned well and the mean distance error of the optimal registration method (CPD with affine transform) was consistently less than 3 mm. The proposed method could effectively fuse the structures from cardiac CTA and SPECT functional images, and demonstrated a potential in assisting in accurate diagnosis of cardiac diseases by combining complementary advantages of the two imaging modalities. © 2024, CC BY.

关键词： Image fusion

Towards unified surgical skill assessment

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arXiv 2021年

作者： Liu, Daochang Li, Qiyue Jiang, Tingting Wang, Yizhou Miao, Rulin Shan, Fei Li, Ziyu NELVT Department of Computer Science Peking University China Peking University Cancer Hospital China Deepwise AI Lab Center on Frontiers of Computing Studies Peking University China Advanced Institute of Information Technology Peking University China

Surgical skills have a great influence on surgical safety and patients' well-being. Traditional assessment of surgical skills involves strenuous manual efforts, which lacks efficiency and repeatability. Therefore, we attempt to automatically predict how well the surgery is performed using the surgical video. In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies. The dependency relationships among these different aspects are specially modeled by a path dependency module in the framework. We conduct extensive experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed framework achieves promising results on both datasets, with the state-of-the-art on the simulated dataset advanced from 0.71 Spearman's correlation to 0.80. It is also shown that combining multiple skill aspects yields better performance than relying on a single aspect. Codes at https://***/JG2OA. Copyright © 2021, The Authors. All rights reserved.

关键词： Surgical equipment

TDEFSI: Theory guided deep learning based epidemic forecasting with synthetic information

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arXiv 2020年

作者： Wang, Lijing Chen, Jiangzhuo Marathe, Madhav Network Systems Science and Advanced Computing Division Biocomplexity Institute and Initiative Department of Computer Science University of Virginia CharlottesvilleVA22904 Network Systems Science and Advanced Computing Division Biocomplexity Institute & Initiative University of Virginia CharlottesvilleVA22904

Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data is updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present TDEFSI1 (Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time series data. During the training phase, TDEFSI uses high-resolution simulations of epidemics that explicitly model spatial and social heterogeneity inherent in urban regions as one component of training data. We train a two-branch recurrent neural network model to take both within-season and between-season low-resolution observations as features, and output high-resolution detailed forecasts. The resulting forecasts are not just driven by observed data but also capture the intricate social, demographic and geographic attributes of specific urban regions and mathematical theories of disease propagation over networks. We focus on forecasting the incidence of ILI and evaluate TDEFSI’s performance using synthetic and real-world testing datasets at the state and county levels in the USA. The results show that, at the state level, our method achieves comparable/better performance than several state-of-the-art methods. At the county level, TDEFSI outperforms the other methods. The proposed method can be applied to other infectious diseases as well. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

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Model-Based Reinforcement Learning for Quantized Federated L...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

CubeSat single-photon detector module for investigating in-orbit laser annealing to heal radiation damage

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arXiv 2024年

作者： Sultana, Nigar Krynski, Joanna Lim, Jin Gyu Floyd, John Lembeck, Michael Makarov, Vadim Kwiat, Paul Jennewein, Thomas Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada University of Illinois Urbana-Champaign ChampaignIL United States Vigo Quantum Communication Center University of Vigo Vigo Spain Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada

Single-photon avalanche photodiodes (SPADs) based on silicon are widely considered for quantum satellite communications but suffer from an increasing dark count rate (DCR) due to displacement damage in their active areas induced by proton radiation. When the DCR of SPADs exceeds a certain threshold, they become unusable for quantum communication protocols. Previous laboratory experiments have demonstrated that laser annealing of SPADs’ active area with about 1 W optical power can significantly reduce radiation-induced DCR of synthetically irradiated SPADs. To assess the feasibility of in-orbit laser annealing on constantly irradiated SPADs in low-Earth orbit, we developed a module with a CubeSat form factor capable of both laser and thermal annealing of four silicon SPADs. Here we report the design and ground testing of this module, investigating laser annealing in a simulated space environment. Our results pave the way for an in-orbit trial that may prove this technology useful for future satellite missions with quantum receivers on board. Copyright © 2024, The Authors. All rights reserved.

关键词： Radiation damage

Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

Practices of machine learning in classification of nanomaterial

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AIP Conference Proceedings 2023年第1期2878卷

作者： Thakar Hitesh Manibhai Mohit Kumar S. Jeyalaksshmi Abdullah Sakti Charan Panda Souvik Pal Himalayan University Itanagar India SkyInstitute Lucknow India Department of Information Technology Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Nalanda Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Sister Nivedita University (Techno India Group) Kolkata India

computer simulations and laptop working furnish balancing methods of figuring out construction associations that are generally focused on towards forecasting the ideal particular shape to make the most of the overall performance in an assumed application. This can be unpredictable with investigational explanations that quantity the shared homes of whole examples of buildings that comprise deliveries or combination of constructions, even when created then treated with attention. Metallic nanoparticle substances are a necessary instance. In this work, we have rummage-sale a multi-stage computing device working workflow to discover the right construction associations of Pt nanoparticles applicable to oxygen decrease, hydrogen corrosion, as well as hydrogen development responses. By which includes organization previous to reversion, we recognized two wonderful lessons of nanoparticles also as a result produced the class-specific fashions based totally on experimentally applicable standards that are regular through interpretations. These multi-structure relations, forecasting houses be around in excess of a massive pattern of constructions, furnish an extra available way to switch data-driven forecasts obsessed by the laboratory.

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