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learning Inertial Measurement Error Compensation in GPS Signal Shielded Using LSTM 22

Learning Inertial Measurement Error Compensation in GPS Sign...

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22nd IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Wu, Yu-Fan Huang, Guo-Shing Kao, Ming-Cheng Natl Chin Yi Univ Technol Inst Elect Engn Taichung Taiwan Hsiuping Univ Sci & Technol Dept Elect Engn Taichung Taiwan

ISBN: (纸本)9781665404037

GPS (Global Positioning System) is an indispensable technology in vehicle positioning and navigation. Now the GPS positioning technology is very mature and is developing towards high-precision and high-reliability technology. However, the stability of GPS needs to be improved This paper uses RTK (Real Time Kinematic) real-time dynamic differential positioning technology that can improve GPS accuracy, as well as basic simple inertial navigation components such as gyroscopes, accelerometers, and magnetic compasses as GPS. Compensation during interruption improves the reliability of GPS positioning. However, the error of the long-term inertial navigation system accumulates over time, which seriously affects the navigation accuracy, and the accuracy of the simple sensor output is not high. Therefore, this paper proposes a neural network-like learning scheme that uses LSTM to achieve high-precision and reliable positioning. We use cars to collect the XY position coordinate data of the original vehicle around the urban area without difference and with difference positioning. Use MATLAB offline operation to calculate lambda (longitude), Phi (latitude) and use the data measured by integrated inertial elements to assist navigation in the road section that is shielded by GPS signals. And use LSTM deep learning to correct its errors, and then compare with and without differential positioning methods to get a more optimized path map to achieve the compensation effect.

关键词： RTK GPS positioning navigation inertial components error compensation LSTM algorithm

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Decision analysis of e-learning in bridging digital divide for education dissemination 26

Decision analysis of e-learning in bridging digital divide f...

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26th international conference on Knowledge-Based and intelligent Information and engineering Systems, KES 2022

作者： Singh, Swapnil Singh, Uma Shankar Nermend, Malgorzata Faculty of Social Sciences Banaras Hindu University Kabir Colony Varanasi22-1005 India Institute of Management University of Szczecin Cukrowa 8 Szczecin71-004 Poland Institute of Pedagogy University of Szczecin Oginskiego 16/17 Szczecin71-415 Poland

The research presented aims to analyze the process of decision making regarding the use of e-learning. The digital divide has been the main consideration, because of the lack of access to the digital devices and services. E-learning plays a vital role in fulfilling the basic requirement and education dissemination by the participation and involvement of individuals. This leads to the development and accessibility of digitally transferred facilities and offers. At this juncture, the study is measuring and justifying the concept with the empirical evaluation to know-about the decision of adoption of e-learning in bridging the digital divide for education dissemination. Primary data has been the main base for the research outcome collected from 605 teachers working at different schools in Poland. The research proposes a solution that the e-leaning is a powerful tool that can bridge the digital divide and is a valid decision. The study concludes that e-learning is a triggering point to educate population digitally, but for sure at the same time infrastructure, technology, equipment, training, and resources are the high value requirements. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th international conference on Knowledge-Based and intelligent Information & engineering Systems (KES 2022)

关键词： Digital devices

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Classification of Stenosis and Regurgitation from Seismocardiogram Signals using Convolutional Neural Network

Classification of Stenosis and Regurgitation from Seismocard...

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international conference on ICT and Knowledge engineering

作者： Moirangthem James Singh L.N. Sharma Samarendra Dandapat Electronics and Electrical Engineering IIT Guwahati Guwahati India

ISBN: (数字)9798350391435

ISBN: (纸本)9798350391442

Timely detection is critical in preventing the advancement of valvular heart diseases (VHD), such as regurgitation and stenosis. Deep learning frameworks present a robust approach for capturing the intricate features of VHD that are often challenging to discern with traditional manual feature extraction techniques. This research presents a convolutional deep learning framework designed to classify stenosis and regurgitation using seismocardiogram (SCG) signals. Our model incorporates four convolutional neural network (CNN) layers designed to extract and learn detailed characteristics of these conditions effectively. Initially, SCG time-series data are segmented to remove artifacts and then converted into SCG images using the normalized power of continuous wavelet transform (CWT) coefficients with analytic morse mother wavelets. The initial CNN layer processes these images to detect low-level features while the remaining layers extract more intricate patterns. Our approach achieves remarkable results, with an overall accuracy of 99.13%, sensitivity of 98.23%, and specificity of 99.42%, based on evaluations involving 17 patients with regurgitation, 18 with stenosis, and 29 healthy controls. This method outperforms current cardiac disease detection techniques, underscoring the effectiveness of SCG signals for early valve disease screening.

关键词： Deep learning Heart Continuous wavelet transforms Accuracy Cardiac disease Feature extraction Wavelet analysis Valves Convolutional neural networks Diseases

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MLRNet: Towards Real-Time Crowd Counting With Mobile-Based Lightweight Framework 19

MLRNet: Towards Real-Time Crowd Counting With Mobile-Based L...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Ji, Peirong Xia, Xiaofeng Wu, Zhiwei Wang, Fusen Liu, Xinyue Sang, Jun Chongqing University School of Big Data & Software Engineering Chongqing401331 China

ISBN: (纸本)9798350346558

The trade-off between efficiency and generalization has been a challenging problem in crowd counting. In this work, we are devoted to suggesting an application-oriented solution to practical device deployment for crowd counting in resource-constrained scenes. In order to optimize the application computation complexity, this paper proposes a counting architecture, called Mobile Lightweight Refine Net (MLRNet), which is targeted for the high-effectiveness of neural networks in real-time crowd counting tasks on light-scale servers or intelligent devices. In the proposed framework, MobileNetV3 is used as the front-end which enables the model of an efficient feature extraction ability with low resource use. Besides, the back-end introduces the design of Light-weight RefineNet, which maintains the overall counting accuracy while compressing the computing costs. Extensive experiments on four mainstream datasets have demonstrated the availability of our proposed network. Particularly, compared with the baseline method, MLRNet achieves higher accuracy while only using around 1/3 of the FLOPS and parameters on three public crowd counting datasets. © 2022 IEEE.

关键词： Economic and social effects

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Adversarial Domain Adaptation for Metal Cutting Sound Detection: Leveraging Abundant Lab data for Scarce Industry data

Adversarial Domain Adaptation for Metal Cutting Sound Detect...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Mir imtiaz Mostafiz Eunseob Kim Adrian Shuai Li Elisa Bertino Martin Byung-Guk Jun Ali Shakouri Department of Computer Science Purdue University West Lafayette USA School of Mechanical Engineering Purdue University West Lafayette USA School of Electrical and Computer Engineering Purdue University West Lafayette USA

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domainingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92 %, 82 % and 85% accuracy respectively for three different sensors installed in industry settings.

关键词： Industries Training Adaptation models Accuracy Milling data models Sensors Manufacturing Labeling Monitoring

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Software Sentiment Analysis Using Machine learning with Different Word-Embedding 22nd

Software Sentiment Analysis Using Machine Learning with Diff...

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22nd international conference on Computational Science and its Applications (ICCSA)

作者： Mula, Venkata Krishna Chandra Vijayvargiya, Sanidhya Kumar, Lov Samant, Surender Singh Murthy, Lalita Bhanu Andhra Univ Coll Engn Visakhapatnam Andhra Pradesh India BITS Pilani Hyderabad Hyderabad India Graph Era Dehra Dun Uttarakhand India

ISBN: (纸本)9783031105487;9783031105470

Software sentiment analysis has applications in numerous software engineering tasks ranging from code suggestions to evaluating app reviews which help to save the development team valuable time and increase productivity. In recent years, sentiment analysis has been used to study the emotional state of developers through sources like commit messages. State-of-the-art sentiment analysis techniques have been employed to accomplish these tasks with varying results. The goal of this paper is to provide a comparison between the performance of various models for possible applications of sentiment analysis in software engineering. We have used three different datasets to account for the possible applications: JIRA, AppReviews, and StackOverflow. In this work, six word embedding techniques have been applied on above datasets to represent the text as n-dimensional vectors. To handle the skewed distribution of classes present in the data, we have employed two class balancing techniques in the form of SMOTE and Borderline-SMOTE. The resulting data is subjected to six feature selection techniques, and finally, the sentiment of the text is classified using 14 different classifiers. The experimental results suggest that some models are very successful in accurately classifying the sentiment of the text, whereas choosing the wrong combination of ML techniques can lead to disappointing performance.

关键词： Sentiment analysis Word embedding SMOTE

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Models of Fuzzy Identification of Cyber Incidents in Information and Communication Systems by intelligent SIEM Systems 22

Models of Fuzzy Identification of Cyber Incidents in Informa...

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22nd international Scientific and Practical conference "Information Technologies and Security", ITS 2022

作者： Subach, Ihor Mogylevych, Dmytro Mykytiuk, Artem Kubrak, Volodymyr Fesokha, Vitalii National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Verkhnoklyuchova str. 4 Kyiv03056 Ukraine Heroes of Kruty Military Institute of Telecommunications and Information Technologies Kyivska str. 45/1 Kyiv01011 Ukraine

The issue of detecting cyber incidents that occur during the functioning of information and communication systems, based on the use of a SIEM system in the cyber protection circuit, as the basis for its design, is considered. This study concludes that the use of the latest information technologies, such as artificial intelligence, intelligent data analysis, big data processing, and machine learning, is necessary to improve cyber incident detection. These technologies make it possible to increase the effectiveness of the SIEM system by minimizing human involvement in solving cyber incident response tasks. The analysis of methods used to detect cyber incidents has revealed their major limitations, particularly their inability to consistently provide the desired outcome in scenarios where information about such incidents is incomplete or inaccurate. To address this issue, a new approach to detect cyber incidents is proposed. This approach is based on using knowledge delivery models within the knowledge base that provide information about the signs of cyber incidents occurring in information and communication systems, as well as the various types of cyber incidents. This knowledge will be used to identify cyber incidents by collecting and processing expert information, utilizing the theory of fuzzy sets. Additionally, the proposed approach includes the formalization of the causal relationships between the variables "signs of cyber incidents" and "types of cyber incidents." This is achieved by describing these relationships in natural language using the theory of fuzzy sets and linguistic variables. The models proposed in this study can improve the effectiveness of cyber incident detection by addressing the limitations of current methods. © 2022 Copyright for this paper by its authors.

关键词： Fuzzy sets

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A Novel Approach for Trajectory Partition Privacy in Location-Based Services

A Novel Approach for Trajectory Partition Privacy in Locatio...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chundong Wang Yongxin Zhao School of Cyber Security and Computer National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Engineering Research Center of Ministry of Education for Learning Intelligent Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Location-based service (LBS) devices have enhanced daily life convenience but raised privacy concerns regarding user location data. For this, this research presents a novel approach to safeguard trajectory privacy through Similarity-based Trajectory Partitioning (STSM). The process begins by dividing trajectory equivalence classes based on distinct timestamps. Trajectory similarity is then assessed within each class using metrics such as Frechet distance, trajectory direction, and speed. Subsequently, a trajectory graph is constructed for each class, and the Dijkstra algorithm is applied to partition the graph using the k-subgraph method, effectively converting trajectory partitioning into graph partitioning. To meet publication requirements, Laplace noise is added to sensitive data linked to nodes and edges within the subgraph. Comparative experiments on real-world data confirm the efficacy, rationality, data availability, and privacy protection capabilities of the proposed trajectory partitioning algorithm, underscoring its superiority over alternative methods.

关键词： Weight measurement Privacy Differential privacy conferences Noise Trajectory Partitioning algorithms

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Prediction Analysis for Diabetic Disease Using Machine learning Model 22

Prediction Analysis for Diabetic Disease Using Machine Learn...

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22nd OITS international conference on Information Technology, OCIT 2024

作者： Senthamarai, N. Masood, Jafar Ali Ibrahim Syed Sathya, M. Chakravarthy, N.S. Kalyan Rathore, Bharati Kalith Oli, Mohamed M. Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Llantwit Rd PontypriddCF37 1DL United Kingdom School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331510404

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. Research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy. © 2024 IEEE.

关键词： Random forests

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An Exact Potential Game-Based End-Edge-Cloud Collaborative Task Offloading Approach 19

An Exact Potential Game-Based End-Edge-Cloud Collaborative T...

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作者： Shen, Yue Liu, Bowen Dai, Fei Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shenzhen China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350346558

With the development of Industrial Internet of Things(IIoT), massive industrial devices produce a large number of delay-sensitive and computing-intensive tasks. Offloading tasks under the end-edge-cloud collaboration mode can improve the efficiency of completing tasks. However, on the one hand, tasks need to be finished before corresponding deadlines and pay for consuming resources, so delay and cost constitute the main factors affecting task offloading decisions. On the other hand, limited real-time resources in the system may not meet the needs of all devices, so we should ensure that as many devices can complete tasks on time as possible while optimizing task delay and cost of each device. To tackle the challenges, we propose an end-edge-cloud collaborative task offloading approach based on exact potential game. Aiming at optimizing delay-cost of each device and task completion rate of the whole system, the end-edge-cloud collaborative task offloading problem is modeled as an exact potential game and solved. Extensive experiments show that the approach can improve the task completion rate of the system while optimizing the delay-cost of each device. © 2022 IEEE.

关键词： Edge computing

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