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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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A Computationally efficient CNN-based Deep Learning Technique for Sickle Cell Detection

A Computationally efficient CNN-based Deep Learning Techniqu...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Hemavarshini, S. Sathya Shree, S. Arumuga Arun, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331522988

An amino acid change in the hemoglobin protein causes sickle cell disease (SCD), a prevalent hereditary illness that causes red blood cells to take on a sickle shape. These malformed cells cause severe health complications, including painful episodes, organ damage, increased infection risk, and a significantly reduced lifespan. Accurate and timely diagnosis is essential for timely therapy and better results for patients. This study investigates the potential of various lightweight deep learning models, MobileNetV1, MobileNetV2, MobileNetV3 Small, and EfficientNetV0 as an efficient and accurate underlying model for SCD detection. All of these trained them on the publicly available Sickle Cell Disease Dataset, where various augmentation methods are used to enhance the resilience and generalization of the models, such as rotating, shifting, shearing, zooming, brightness, and contrast adjustments. The MobileNetV1 has achieved better performance with an accuracy of 93.2% and F1Score of 95.43% with a lesser complexity with computational parameters of 4.855 Million and a model size of 18.52 MB. These findings highlight the potential of these lightweight models to revolutionize SCD screening and diagnosis in resource-constrained environments, offering faster, more accessible, and cost-effective diagnostic tools, ultimately improving healthcare outcomes for individuals with SCD. © 2024 IEEE.

关键词： Patient treatment

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Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning 41

Online Matching with Stochastic Rewards: Provable Better Bou...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Qiankun Shen, Aocheng Zhang, Boyu Jiang, Hanrui Du, Bingqian School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

For a specific online optimization problem, for example, online bipartite matching (OBM), research efforts could be made in two directions before it is finally closed, i.e., the optimal competitive online algorithm is *** is to continuously design algorithms with better *** this end, reinforcement learning (RL) has demonstrated great success in ***, little is known on the other direction: whether RL helps explore how hard an online problem *** this paper, we study a generalized model of OBM, named online matching with stochastic rewards (OMSR, FOCS 2012), for which the optimal competitive ratio is still *** adopt an adversarial RL approach that trains two RL agents adversarially and iteratively: the algorithm agent learns for algorithms with larger competitive ratios, while the adversarial agent learns to produce a family of hard *** such a framework, agents converge at the end with a robust algorithm, which empirically outperforms the state of the art (STOC 2020).Much more significantly, it allows to track how the hard instances are *** succeed in distilling two structural properties from the learned graph patterns, which remarkably reduce the action space, and further enable theoretical improvement on the best-known hardness result of OMSR, from 0.621 (FOCS 2012) to *** the best of our knowledge, this gives the first evidence that RL can help enhance the theoretical understanding of an online problem. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Learning intrinsic characteristics of environment for generalization in deep reinforcement learning 3

Learning intrinsic characteristics of environment for genera...

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3rd International Conference on Electronic Information engineering, Big Data, and computer technology, EIBDCT 2024

作者： Li, Sisi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510680449

In most real-world open environments, deploying reinforcement learning algorithms poses significant challenges. Since models trained through reinforcement learning are sensitive to environmental parameters, strategies trained in one environment may not perform well in unseen environments. Existing methods primarily focus on increasing the similarity between training and testing data or directly dealing with feature differences between training and testing, however, these methods suffer from issues such as lack of diversity and prone to overfitting. In this work, we propose a novel method called Bidirectional Prediction with Randomization (BPR). Based on randomization of the lower-level inputs of the network, BPR utilizes a discriminator for environment forward and inverse dynamics prediction, aiming to enhance the generalization performance of deep reinforcement learning algorithm models by learning the intrinsic characteristics of the environment. We conduct experiments on all environments of OpenAI Procgen benchmark, and the results demonstrate that our method performs well in game scenarios. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Reinforcement learning

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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas Sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Harshavardhan, S. Upender, Patri Ganesh, N. Giftsy, Sharon Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798331529833

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology. © 2025 IEEE.

关键词： Chemical sensors

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High-Fidelity Real-Time Radar Modeling with Consideration of False and Missed Alarms for Autonomous Driving Simulation

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Huang, Kaibo Deng, Weiwen Wang, Ying Zhao, Rui Ding, Juan School of Transportation Science and Engineering Beihang University Beijing China College of Computer Science and Technology Jilin University Jilin China PanoSim Technology Company Limited Jiaxing Zhejiang China

Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models. IEEE

关键词： Radar cross section

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A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

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computer Modeling in engineering & sciences 2023年第6期135卷 1901-1940页

作者： Geng Chen Lili Cheng Rui Shao Qingbin Wang Shuihua Wang College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao266590China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide *** leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of *** and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in *** that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological *** paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological ***,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are ***,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are *** review has provided comprehensive and effective tracking and positioning techniques,technologies

关键词： Home-based care device-free neurological diseases indoor positioning positioning techniques and technologies positioning accuracy

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Robust Node Embedding Based on Subspace Attribute Complement Topology 4

Robust Node Embedding Based on Subspace Attribute Complement...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Kang, Jiahua Deng, Ziling Xi, Hongqing Wang, Xiaoxiao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350359589

Graph Neural Networks (GNNs) have been proven to be powerful tools for graph analysis. However, despite their success, existing GNNs are often sensitive to the quality of the input graph. Real-world graphs are typically noisy, often represented topologically as containing some edges irrelevant to the task or missing some edges, which can lead to sub-optimal generalization performance of the learned GNNs models. To address this issue, we introduce a robust node representation algorithm for attribute networks based on the concept of subspace (STSAI) algorithm. Adequate experiments were also done on the real-world dataset to verify the validity of this work. © 2024 IEEE.

关键词： GNNs robust subspace

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Heterogeneous network impulsive dynamics for identifying disease-associated genes

Heterogeneous network impulsive dynamics for identifying dis...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xiang, Ju Chen, Shengkai Wang, Lin-Cong-Hua Meng, Xiangmao Li, Min Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Central South University School of Computer Science and Engineering Changsha China Xiangtan University School of Computer Science Xiangtan China

ISBN: (纸本)9798350386226

Identifying disease-associated genes (DAGs) is important for the research of complex diseases, and network-based methods have been a powerful and elegant strategy for this topic. Genes and their products perform biological functions through synergy in biological networks, but mining useful information from the networks remains an open issue. Therefore, we propose a novel heterogeneous network impulsive dynamics model to identify DAGs more effectively. It inspires a heterogeneous network impulsive dynamical process by imposing impulsive signals at specific nodes, in an enhanced dual-layer heterogeneous network. Then, it extracts the impulsive dynamical signatures of responses of nodes to the impulsive signals so as to infer DAGs. A series of experiments confirm that this model has good performance of inferring DAGs under different conditions, and case studies further demonstrate its effectiveness. Furthermore, a user-friendly web platform is provided to facilitate prioritization and analysis of DAGs. It may become a useful tool for studying complex diseases and relevant genes. © 2024 IEEE.

关键词： Genome

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An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma

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SN computer science 2024年第5期5卷 1-17页

作者： Veena, H.N. Patil, Kavita K. Vanajakshi, P. Ambore, Anilkumar Gowda, Naveen Chandra Department of Computer Science and Engineering SJB Institute of Technology Bengaluru India Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Information Science and Engineering Vivekananda Institute of Technology Bengaluru India School of CSE REVA University Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

In the current medical implications, one of the leading ocular diseases is Glaucoma which majorly damage the Optic Nerve Head (ONH) of the eye retina. The intraocular pressure of the eye leads to glaucoma, which may lead to complete or partial vision loss. Regular screening and early detection is the only solution to avoid further vision loss. Due to the laborious and manual procedure in diagnosis, an automatic system is needed to diagnose glaucoma. This paper presents a novel method using deep learning-based RNNLSTM classification model to develop an automatic approach to predict and classify the images to be as healthy or glaucomatous. The RNN and LSTM with dense, dropout, and batch normalization layers are used for training and testing the proposed prediction model. The RNN model is used for training the model and to overcome the problems that occur during training, the LSTM model is applied to increase the performance of the model. The proposed model achieved an accuracy of 97.4%, specificity of 97.9% and sensitivity of 97%in classifying the images. We have made use of the DRISHTI-GS database for training and testing the proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Glaucoma Long short-term memory (LSTM) Recurrent neural network (RNN)

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