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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bachal, Tarush Kumar, Ashwin Shetty, Ansh Pawar, Rajendra School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India

ISBN: (纸本)9798350304268

This paper describes the problem of predicting the occurrence of West Nile Virus (WNV) in mosquitoes in the city of Chicago. Each year, from late May to early October, mosquito traps are set up across the city, and the mosquitoes collected in the traps are tested for the presence of WNV. The goal of this is to predict where and when different races of mosquitoes will test positive for the WNV based on climate, area, and testing information. A more accurate prediction method could help the city allocate resources more effectively towards preventing transmission of this potentially dangerous infection. This paper includes a literature review of previous studies on the transmission of WNV and the vectorial capacity of mosquitoes. The paper also provides details on the data collection process, including the mosquito trapping, testing, and spray data, as well as the weather data. © 2023 IEEE.

关键词： Climate change Data analysis Disease prevention Disease surveillance Machine learning Mosquito behavior Mosquito control Mosquito species Mosquito-borne diseases Precipitation Predictive modeling Public health Temperature West Nile virus

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Aerial image matching based on dynamic convolution and adaptive neighborhood consensus network 8

Aerial image matching based on dynamic convolution and adapt...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Tao, Yu Yu, Wanjun Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai201418 China

ISBN: (纸本)9798350369564

Aerial image matching is the process of geometrically aligning two or more images under different environments (time, angle, sensor changes). Aiming at the problems of low matching accuracy and weak robustness of aerial images in matching and alignment tasks, we improve an algorithm for feature extraction combining dynamic convolution and pre-trained residual networks. In the matching stage, an adaptive domain consensus network is adopted to enhance the matching accuracy. Dynamic convolution replaces the convolution in the original Resnet101 residual block and combines it with other pre-trained convolutional layers for feature extraction, which can extract deep fine features. The bidirectional matching network is designed and the adaptive neighborhood consensus network is used to filter the mismatched point pairs, which can obtain higher matching accuracy and lower matching error. Experimental results show that the model is better than other models under multiple evaluation indicators of aerial image matching. © 2023 IEEE.

关键词： Convolution

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A Distributed Hyperspectral Target Detection Algorithm Based on Background Reconstruction for Cloud Platforms 13

A Distributed Hyperspectral Target Detection Algorithm Based...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Xu, Lidan Wu, Zebin Sun, Jin Zhang, Yi Wei, Zhihui Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350395570

Target detection is an important research direction in the field of hyperspectral image interpretation. This paper proposes a Spark-based distributed and parallel implementation of a hyperspectral target detection method that integrates variational autoencoder with constrained energy minimization to detect the residual image. Our proposed method can significantly reduce the computation time in hyperspectral target detection by means of distributed computing and is scalable with the increasing size of hyperspectral data. In addition, we introduce an AUC score-based data pruning (ASDP) strategy as well as its distributed version that facilitates parallel processing in our proposed method. Specifically, we use a data pruning scoring criterion to evaluate the hyperspectral data and remove redundant data with insignificant impact on the final training accuracy. Experimental results on several real-world hyperspectral datasets demonstrate that the distributed and parallel implementation can reduce the total amount of computational data for target detection without accuracy degradation. © 2023 IEEE.

关键词： Radar target recognition

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iDANSE: Iterative Data-driven Nonlinear State Estimation of Model-free Hidden Sequences

iDANSE: Iterative Data-driven Nonlinear State Estimation of ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Hang Ghosh, Anubhab Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350368741

We introduce a model-free hidden sequence (MHS) estimation problem where the task is to estimate a long sequence of 'model-free' process hidden under additive Gaussian noise. To estimate the posterior of the hidden sequence from the noisy observation sequence, we have three main challenges: (a) the process is 'model-free', that means there is no underlying state-space-model (SSM), limiting the use of traditional SSM-informed model-based methods like Kalman filter (KF) and particle filter (PF);(b) only an unlabelled dataset comprised of noisy observation sequences is available as training data, and hence no supervised learning possible;and, (c) the use of sequential ancestral sampling based methods, like dynamical variational autoencoders (DVAEs), results in prohibitive complexity. To address the challenges we adopt a recently established recurrent neural network (RNN)-based method called DANSE (data-driven nonlinear state estimation). We develop an iterative DANSE (iDANSE) in unsupervised learning setup where a set of neural networks (NNs) are used iteratively. The set of NNs refines MHS estimation over the iterations. Using simulations, the performance of iDANSE is demonstrated for a benchmark Lorenz process (a chaotic attractor) and compared with SSM-informed extended KF (EKF) and unscented KF (UKF). © 2025 IEEE.

关键词： deep neural networks Kalman filter sequential modelling State estimation unsupervised learning

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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Limited Resources Multi-Object Tracking by Utilizing Motion Cue from the Meanshift Algorithm 9

Limited Resources Multi-Object Tracking by Utilizing Motion ...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Chang, Heming Han, Shumin Shi, Zhiguo Hu, Siquan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing10083 China

ISBN: (纸本)9798350383706

Multi-object tracking has various applications in intelligent surveillance systems. Substantial advancements have been made to enhance the accuracy of multi-object tracking. Nevertheless, many existing approaches struggle with real-time tracking in resource-constrained environments such as surveillance centers. While it's possible to get close to real-time tracking with low resources utilizing motion-based trackers, their framerates are frequently limited by detectors. In this article, we introduce a technique that reduces the frequency of object detectors and association algorithms calls in SORT-like trackers by performing target detection and data association every several frames, predicting the positions of bounding boxes between target detections using motion cues derived from the meanshift algorithm. By this mean, we achieved real-time multi-object tracking with an average end-to-end framerate of 38.09 fps on the MOT17 dataset on a single mobile RTX4060 65W, which is 230% of the original SORT implementation while maintaining most of the precision metrics evaluated in IDF1 and MOTA, significantly increasing the tracking framerate in resource-constrained scenarios. © 2023 IEEE.

关键词： Object detection

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2022 International Symposium on Design Studies and Intelligence engineering, DSIE 2022

作者： Zhang, Kaiqiang Li, Wei Yu, Shaoyong Chen, Shunyi Zeng, Zhiqiang Ma, Xiaoyu Luo, Youmeng School of Computer and Information Engineering Xiamen University of Technology China School of Mathematical and Information Engineering Longyan University China Center of Modern Engineering Training Xiamen University of Technology China

ISBN: (纸本)9781643683720

In this paper, we propose a similar image retrieval algorithm based on feature fusion and locality-sensitive hash to address the problems of inadequate representation of image content by individual features and long retrieval time for massive image data. The fusion of global features and attention features makes the image features have both color structure and semantic information, which can better characterize the image content. In the image retrieval stage, the locality-sensitive hash is used to hash encode the image features, the cosine similarity is used as the similarity measure, and finally, the index is built to improve the retrieval efficiency. The similar image retrieval algorithm proposed in this paper has improved the average finding accuracy and recall rate on Caltech 256 and Corel5k datasets compared with other methods, and the retrieval time is greatly reduced. © 2023 The authors and IOS Press.

关键词： Image retrieval

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Adversarial Attacks and Defences of Various Artificial Intelligent Models 2

Adversarial Attacks and Defences of Various Artificial Intel...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Singh, Tanmay Rai, Parijat Sood, Saumil Nigam, Siddhant Kumari, Suchi School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India

ISBN: (纸本)9798350305418

Adversarial attacks are becoming increasingly common and sophisticated as time passes and new defenses are being researched and developed. Adversarial machine learning is one of the techniques used to attack artificial intelligence models by feeding deceptive data. Therefore, in this paper, adversarial attacks against machine learning and deep learning are studied, and some defense strategies are also proposed against them. Two attack strategies;Evasion and Poison are used to attack the Support Vector Machine (SVM) model and Convolution Neural Network (CNN) model, respectively. Some defense mechanisms are applied to safeguard the models from such attacks. The models are applied and tested on the MNIST dataset. The results obtained from the models, before and after the defenses are applied are compared. © 2023 IEEE.

关键词： Adversarial attack deep learning Evasion attack machine learning Poison attack

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Classification of Malware Using Deep Learning: A Study 56

Classification of Malware Using Deep Learning: A Study

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Srinu, Inakollu Shanmukha Vidyarthi, Deepti School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India

ISBN: (纸本)9798350315875

Malware, or malicious software, significantly threatens data privacy and system security. This study delves into image-based malware classification, employing deep learning techniques. This research addresses the early stages of the classification of malware using image-based deep learning approaches. Our study emphasizes the exploration of diverse methodologies to enhance malware classification efficiency. We acknowledge image-based classification, with substantial potential for improved accuracy and robustness in malware classification. Additionally, we conduct a comprehensive comparative analysis, contrasting our image-based approach with existing methods. Through rigorous experimentation, we achieve 97.3% accuracy rate, highlighting the effectiveness of our approach. In conclusion, the study aims to contribute to evolving cybersecurity practices by exploring this domain for more precise and efficient malware classification. © 2023 IEEE.

关键词： Malware

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Low-Cost Smart Plant Irrigation Control System Using Temperature and Distance Sensors 5th

Low-Cost Smart Plant Irrigation Control System Using Tempera...

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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Malla, Jyotsna Jayashree, J. Vijayashree, J. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789811986680

This study revolves around the idea of proposing a smart and low-cost automated irrigation control system. This system attempts to effectively provide an end-to-end solution to irrigation problems by using economical and easily accessible components. Farmers using traditional irrigation systems primarily suffer from the lack of automation tools and the mechanisms to deal with environmental problems. This paper aims to solve the limitations of traditional irrigation systems using commonly used components in a cost-efficient manner. A synergy between temperature and distance sensors is used to manage the motor and hence automate the plant irrigation system. The system is equipped with an actuator (piezo) to shut down the motor and alert the farmer in case of reduced water supply in order to prevent problems arising from depleted underground water levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Liquid crystal displays

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