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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Liu, Yang Li, Sufang Yu, Jiguo Dong, Anming Zhang, Li Zhang, Chuanting Cao, Yi Qilu University of Technology School of Computer Science and Technology Jinan China Big Data Institute Qilu University of Technology Jinan China University of Bristol Department of Electrical and Electronic Engineering Bristol United Kingdom University of Jinan School of Information Science and Engineering Jinan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Sensing human activity via WiFi Channel State Information (CSI) has considerable application prospects in future intelligent interaction scenarios such as virtual reality, intelligent games, metaverse, etc. Recently, many Deep Learning-based WiFi sensing schemes have been proposed in the literature, which gained high accuracy for a wide range of simple activities such as standing, squatting, and bending. However, the performance will be suffered when existing approaches are used to recognize drastic activities, such as actions in vigorous sports. This is mainly due to the reason that the spatiotemporal information of these actions is not well utilized. To overcome this drawback, we propose a novel DL-based WiFi sensing method for drastic activity recognition by combining the Convolutional Neural Network (CNN) and the Bidirectional Long Short-Term Memory (BiLSTM) network. The designed CNN-BiLSTM architecture is in parallel with feature extraction, which can simultaneously extract sufficient spatiotemporal features of action data and establish the mapping relationship between actions and CSI streams, thereby improving the accuracy of activity recognition. The CNN is used to extract information on the spatial dimension, while the BiLSTM extracts information on the time dimension. To verify the performance of the proposed scheme, we build a hardware experiment platform and constrain a dataset with 1400 pieces of records for 7 classes of basketball actions. After training over the dataset, the proposed CNN-BiLSTM scheme achieves 96% experimental accuracy on the test set, which is better than the benchmark methods. © 2022 IEEE.

关键词： Convolutional neural networks

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Perceptual quantization parameter selection for crime scene investigation tool images

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Frontiers of computer science 2023年第1期17卷 241-243页

作者： Yanchao GONG Zhao LI Zhuang WANG Kaifang YANG Ying LIU Keng Pang LIM School of Communications and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China School of Electronic and Information Engineering Chongqing Three Gorges UniversityChongqing 404000China School of Computer Science Shaanxi Normal UniversityXi’an 710119China XsecPro Pte.Ltd. Singapore 787820Singapore

1 Introduction Crime scene investigation(CSI)is critical for solving criminal cases and court ***,all CSI data must be kept as electronic files in specific systems according to relevant *** files,including reconstructed CSI images,provides great convenience for the public security and court to store,manage,display,and analyze CSI data more *** coding which pursues higher reconstructed quality of CSI images using lower coding rate plays a very important role in the above *** with the previous image coding standards,high efficiency video coding(HEVC)still image coding technique significantly improves the image coding *** coding efficiency of HEVC is closely related to the selected quantization parameter(QP).

关键词： parameter image CSI

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Automated Artificial Intelligence Empowered White Blood Cells Classification Model

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computers, Materials & Continua 2023年第4期75卷 409-425页

作者： Mohammad Yamin Abdullah M.Basahel Mona Abusurrah Sulafah M Basahel Sachi Nandan Mohanty E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)VisakhapatnamAndhra Pradesh530049India

White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.

关键词： White blood cells cell engineering computational intelligence image classification transfer learning

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DL-BERT: a time-aware double-level BERT-style model with pre-training for disease prediction

DL-BERT: a time-aware double-level BERT-style model with pre...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Chen, Xianlai Lin, Jiamiao An, Ying Central South University Big Data Institute Changsha China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9781665480451

Disease prediction based on the electronic Health Record (EHR) is an important task in healthcare. EHR records patients' every visit by time, and there are many kinds of medical codes within a visit, therefore, EHR has characteristics of temporal irregularity and hierarchical structure. Some recent works employ BERT-style models to process EHR data for disease prediction. However, few of these models can give consideration to capture both the interaction between medical codes and the impact of temporal irregularity. To solve this problem, we propose the Double-Level BERT-style model (DL-BERT). Considering EHR's hierarchical structure, the model contains a code-level and a visit-level representation layer which can learn the relationship between medical codes and temporal influence respectively. In the code-level representation layer, the model achieves the representation power by employing external medical ontologies to provide multi-resolution information of medical codes and the Transformer to embed medical codes. Besides, the model adopts two pre-training tasks to enhance the ability to capture the link between different kinds of codes. In the visit-level representation layer, DL-BERT utilizes a special time-aware Transformer to model temporal information. And the model adopts a visit-level pre-training task for better learning context information. Experiments are conducted on two real-world healthcare datasets and show that our model outperforms all baselines demonstrating the effectiveness of our model. © 2022 IEEE.

关键词： Forecasting

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1248-1262页

作者： Dongdong Zhao Qiben Xu Fan Li Yiheng Mao Jianwen Xiang Huanhuan Li Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence and Chongqing Research Institute Wuhan University of Technology Wuhan China School of Computer Science School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

With the development of genome sequencing technology, genomic data have been widely collected and used in real-world scenarios, such as genomic medicine and similar patient query (SPQ) cases, based on similarity comparisons of genomic sequences. However, genomic data are unique for every person and contain a large amount of sensitive information involving personal privacy. Therefore, determining how to protect privacy while utilizing genomic data has become a key issue. In this paper, we mainly investigate the privacy protection of SPQs, and we propose five algorithms (NDB-ED, NDB-Band, NDB-Block, NDB-SIS and NDB-SDS) based on a promising technique called negative representation of information (NRI). The proposed algorithms use five kinds of similarity comparison approaches widely applied in SPQs and convert all genomic sequences into negative databases ( NDBs, among the most important NRI forms) for privacy protection. When performing an SPQ, NDB-ED approximates the edit distance between the sketches (the statistics of NDBs) of two genomic sequences to evaluate the dissimilarity. Banded edit distance and block-wise edit distance are two effective approximate edit distance algorithms, which can greatly reduce the time complexity. NDB-Band and NDB-Block are used to estimate the banded edit distance and the block-wise edit distance between the sketches of NDB pairs, respectively, to further improve query efficiency and reduce communication overhead. Besides, private genome set intersection size (SIS) and set difference size (SDS) can also be used instead of edit distance to evaluate the dissimilarity between genomic pairs during SPQs. NDB-SIS and NDB-SDS estimate the SIS and SDS between the sketches, for similarity comparison. The experimental results demonstrate that the proposed algorithms can achieve promising results in terms of accuracy and efficiency (Our best algorithm improves accuracy by at least 10% and query time is at least 700 times faster than existing a

关键词： Bioinformatics Genomics Protection databases Protocols Privacy data privacy Accuracy Time complexity Hamming distances

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 1-18页

作者： Huang, Xuegang Yin, Chun Dadras, Sara Cheng, Yuhua Bai, Libing Hypervelocity Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Electrical and Computer Engineering Department Utah State University LoganUT84321 United States

To enhance the detection efficiency in eddy current pulsed thermography, an adaptive feature extraction algorithm for defect identification is developed in this paper. The proposed algorithm involves four stages, namely, the thermal image segmentation, the variable interval search, the distance correlation clustering analysis and the between-class distance decision making. The transient thermal responses (TTRs) with similar characteristics are collected into one data block. The thermal image segmentation and variable interval search can help reduce the repetitive calculation in defect identification by choosing local optimums in each data block. The global optimum that has the largest sum of the between-class distance, is derived by first classifying the local optimums and then calculating the correlation distance of the thermal responses with the center points of each class. Finally, the TTRs with the largest between-class distance are regarded as the typical ones which can be used to identify the discriminative defect features of infrared image sequence. Finally, the comparison experiments are carried out to demonstrate the effectiveness and advantages of the proposed approach. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： K-means clustering

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Hands-Free: Action Abstraction With Hierarchical Reinforcement Learning in Text-Based Games

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IEEE Transactions on Consumer electronics 2024年

作者： Zhu, Anjie He, Hongcai Yang, Yongjun Zheng, Zetao Shao, Jie University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China

Large-scale knowledge discovery and decision-making based on textual data are crucial in consumer electronics. For instance, intelligent home robots analyze textual data to tailor household tasks to user preferences, enhancing smart home convenience and comfort. Text-based games offer a research platform for testing and developing artificial intelligence algorithms. These games rely on written narratives and textual interactions while presenting large and combinational action space, and partial observability issues to agents. However, previous methods relied on pre-specifying the subtasks or pre-training on datasets derived from human gameplay, requiring prior knowledge and manual configuration. In this paper, we introduce a novel action abstraction method to free our hands based on the option framework, which uses temporally extended macro-actions to encapsulate relevant long-horizon behaviors. Through leveraging options, our framework decomposes complex tasks autonomously without pre-specified subtasks or demonstrations, thereby partially alleviating the obstacles posed by large and combinatorial action spaces. Moreover, for partial observability issue, we employ a bi-directional attention mechanism to estimate state-action value accurately, strengthening the interdependence between state and action. We conduct extensive experiments on text-based games and the results show the effectiveness of our proposed framework, highlighting its potential in enhancing intelligence in consumer electronics. © 1975-2011 IEEE.

关键词： Adversarial machine learning

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A Blockchain-based Predictive Maintenance Scheme for Smart Agriculture

A Blockchain-based Predictive Maintenance Scheme for Smart A...

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作者： Mohil Patel Sudeep Tanwar Anish Jindal Naif Alshammari Haris Pervaiz Hasan Ahmed Institute of Technology Nirma UniversityDepartment of Computer Science and EngineeringGujaratIndia University of Durham Department of Computer ScienceUnited Kingdom Lancaster University School of Computing and CommunicationsUK University of Essex School of Computer Science and Electronic EngineeringUK

With technology invading all the sectors, the agriculture sector is not alien to technological developments and advancements. The use of IoT, robotics, sensors, artificial intelligence, etc., has resulted in the concept of Smart Agriculture, which aims to decrease human effort and increase the quantity and quality of the products. A lot of machinery is being used in smart agriculture and its maintenance is an important aspect for continued productivity and efficiency. Implementing a predictive maintenance scheme will reduce the high cost in terms of money and time and decrease unexpected faults and breakdowns. Motivated from the aforementioned discussion, in this paper, we propose a blockchain-based predictive maintenance scheme that utilizes the data collected by sensors to monitor and predict the condition of machines. The proposed scheme that uses K-Nearest Neighbours, Random Forest and LSTM models have been used and their performance has been compared based on accuracy, precision, recall, and F1-score. Results show that Random Forest achieved an F1-score of 89%, which is the best performance among all the models and can be effectively utilized for this purpose.

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