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Enhancement of Sensor-based User Identification using Data Augmentation Techniques 7

Enhancement of Sensor-based User Identification using Data A...

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7th international conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

Mobile devices such as smartphones are increasingly being used to record personal, delicate, and security information such as images, emails, and payment information due to the growth of wearable computing. It is becoming more vital to employ smartphone sensor-based identification to safeguard this kind of information from unwanted parties. In this study, we propose a sensor-based user identification approach based on individual walking patterns and use the sensors that are pervasively embedded into smartphones to accomplish this. Individuals were identified using a convolutional neural network (CNN). Four data augmentation methods were utilized to produce synthetically more data. these approaches included jittering, scaling, and time-warping. We evaluate the proposed identification model's accuracy, precision, recall, F1-score, FAR, and FRR utilizing a publicly accessible dataset named the UIWADS dataset. As shown by the experiment findings, the CNN with the time-warping approach operates with very high accuracy in user identification, with the lowest false positive rate of 8.80 % and the most incredible accuracy of 92.7%. © 2022 IEEE.

关键词： Smartphones

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Face Attribute Recognition Based on Task Similarity and Attention Module 7

Face Attribute Recognition Based on Task Similarity and Atte...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Liu, Yingfang Wang, Song Ma, Yatong Xie, Tian School of Electronic and Information Engineering Lanzhou Jiaotong University Gansu Lanzhou China

ISBN: (纸本)9781665478571

Face attributes related single-task recognition technology has become mature, however, the research on deep multi-task learning in the field of face attribute recognition is not enough. Aiming at the problem that single-task recognition ignores the correlation of different attributes and the subjectivity of deep multi-task model construction, a model construction method is proposed for recognition of multiple attributes of human face simultaneously. Firstly, CKA is introduced to calculate the similarity between different single-task models, and the shared parts and branch parts of the deep multi-task model are determined;then, a deep multi-task learning model is constructed, realizing information sharing through parameter sharing in the shallow layer, and completing the identification of specific attributes by setting different task output layers in the deep layer, through attention module to better accomplish the recognition of specific attributes. Finally, the effectiveness of this method is verified on the public datasets Adience and UTKFace, and more optimal experimental results are obtained for gender and age recognition. © 2022 IEEE.

关键词： Deep learning

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Honey Bee Sound Classification Using Spectrogram Image Features 7

Honey Bee Sound Classification Using Spectrogram Image Featu...

引用

作者： Mekha, Panuwat Teeyasuksaet, Nutnicha Sompowloy, Tanapong Osathanunkul, Khukrit Maejo University Faculty of Science Department of Computer Science ChiangMai Thailand The Fifth Regional Livestock Office Department of Livestock Development ChiangMai Thailand ChiangMai Thailand Payap University International College Department of Information Technology ChiangMai Thailand

ISBN: (纸本)9781665495103

the main problem for beekeeping farmers in thailand or around the world is lack of work skills or work experience. this is because beekeeping is a very delicate process and must be carefully monitored with the environment that is changing. the honey bee is known as the European honey bee (Apis mellifera). In thewhole life of honey bee, sound plays an important role in the development of key elements of the honey bee colony. Factors or beehive situations that affect beekeeping include: 1. A lost Queen 2. Smoke 3. Enemy attack 4. Mites 5. Lacks pollen and 6. Normal. In our research, we developed a process of sound classification of a honey bee colony in each situation in bee boxes with spectrogram image features. We examined these in 6 groups to train the classification models. the achieved accuracy was 99.82% in Logistic Regression. the result shows high efficiency of understanding honey bee sound classifications using the spectrogram image feature. © 2022 IEEE.

关键词： Smoke

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Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN-LSTM) Model 7

Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN...

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7th international conference on Machine Learning and Natural Language processing, MLNLP 2024

作者： Yu, Bo Shan, Yiyu Abdalla, Hemn Barzan Ihnaini, Baha School of Mathematical Sciences College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China College of Science Mathematics and Technology Wenzhou-Kean University IAINLP Department of Computer Science Wenzhou China

ISBN: (纸本)9798350354973

With the proliferation of Internet technology and social media platforms, information can be easily exchanged across regions at unprecedented speeds, but this can also reduce the quality of information - especially in an era when false and misleading content spreads like wildfire on social media. False news spreads among people, misleads individuals and causes great harm in politics, economy, public health, and other fields. Despite the challenges of fake news detection and intervention, excellent technical support is provided in this regard thanks to recent advances in big data, natural language processing (NLP), deep learning paradigms, and more. Currently, however, the technology that can be used to determine whether news is true or false has been developed primarily in English - and there is an urgent need to optimize doing this in China at a non-minimal scale and complexity. the motivation and purpose of this study is to achieve the same high accuracy of fake news detection in the Chinese context. this article suggests a high-precision Chinese fake news detection system that innovatively combines the ERNIE 3.0, TextCNN, and BiLSTM models. Each model has a distinct role in our system: ERNIE 3.0 collects sophisticated semantic information using its robust pre-trained language model, which is particularly useful for comprehending Chinese text;TextCNN is good at extracting local features from text and identifying essential patterns. BiLSTM excels in identifying long-distance dependencies and contextual information in text. We employ a progressive training technique, initially training and optimizing the performance of each model independently, and then in the integrated model by weighting the outputs of these independent models so that they learn from one another, hence enhancing overall predictive power. As a result, our model can effectively integrate the benefits of each, improve the complete analysis capability of the complex characteristics of fake news, and i

关键词： Semantics

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Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm 1

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7th international conference on computer Vision and Image processing, CVIP 2022

作者： Kumar, V. Sathiesh Pearline, S. Anubha Department of Electronics Engineering MIT Campus Anna Universi ty Chennai India Department of Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Real-time plant species recognition under unconstrained environment (viewpoint variation, changing background, scale variation, illumination changes etc.) is a challenging and time-consuming process. In this paper, a non-averaged DenseNet-169 (NADenseNet-169) CNN architecture is proposed and demonstrated to perform real-time plant species recognition. the architecture is evaluated on two datasets namely, Flavia (Standard) and Leaf-12 (custom created). the hyperparameters (optimizers, learning rate) are optimized to achieve higher performance metrics with lower computation time. From the experimental investigation, it is observed that Adam optimizer with a learning rate of 0.0001 (Batch size of 32) resulted in obtaining higher performance metrics. In case of Flavia dataset, an accuracy of 98.58% is obtained with a computational time of 3.53 s. For Leaf-12 dataset, an accuracy of 99% is obtained with a computational time of 4.45 s. the model trained on Leaf-12 dataset performed better in identifying the plant species under unconstrained environment. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Plant diseases

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Campus Placement and Salary Prediction: Leveraging Machine Learning for Enhanced Employability 7th

Campus Placement and Salary Prediction: Leveraging Machine L...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Jayashre Raahul, R. Roahith, R. Ganesan, Shanmathi Department of Computer Science and Engineering Shiv Nadar University Chennai India

ISBN: (纸本)9783031699856

In an era of fierce talent competition, optimizing campus placement and predicting graduate salaries is vital. this paper explores ‘Campus Placement and Salary Prediction’ using SVM, Random Forest, Logistic Regression, KNN, and Gradient Boosting. We analyze a comprehensive dataset with student info, academics, skills, internships, and placement outcomes to identify success factors. We employ a majority voting rule among these models and have created a user-friendly website for practical use in academic institutions. Our multifaceted research supports institutions in enhancing student employability and aligning academic goals with the evolving job market. Logistic Regression, one of the models employed has campus placement prediction, has an accuracy of 84%, whereas Gradient Boosting, one of the models used for salary estimation, has an accuracy of 78%. © IFIP international Federation for information processing 2024.

关键词： Students

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A Survey of Object Detection Techniques for Improving Smart Surveillance 7

A Survey of Object Detection Techniques for Improving Smart ...

引用

7th international conference on Image information processing, ICIIP 2023

作者： Sharma, Himani Kanwal, Navdeep Punjabi University Department of Computer Science & Engineering Punjab Patiala India

ISBN: (纸本)9798350371406

Surveillance of our everyday lives is becoming a necessity. Moreover, using humans to keep an eye on the intrusion is a time-consuming and exhausting process. the media almost consistently reports on new monitoring activities that have just come to light. Afterward, security is a persistent challenge among nations, thus steps are indeed being taken to increase protection from unusual happening. Furthermore, the expansion emerged drone surveillance technologies and compositions of several kinds of tools for object detection, evidently to fight crime and terrorism but in actuality employed for many other, sometimes invasive, objectives. Smart measures employing technology are being deployed with physical fences to heighten the vigilance of security personnel at the territories. the unauthorized artifacts may be identified in any video sequence using object detection methods. In order to increase the safety of a video surveillance system, this article provides a summary of the existing techniques. the primary goal of the literature survey is to identify the different approaches to both static and motion object recognition and tracking for smart surveillance. In addition, significant challenges and usages for Object Detection techniques are discussed in detail. © 2023 IEEE.

关键词： Object detection

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Implementation of Energy efficient Blockchain using RUST 7

Implementation of Energy efficient Blockchain using RUST

引用

7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Chaurasia, Amit Krishnamachari, Dhruv Rajesh Manipal University Jaipur Department of Computer and Communication Engineering Rajasthan Jaipur303007 India

ISBN: (纸本)9781665454018

Blockchain technology has become a popular technology since the introduction of WWW. With the introduction of Blockchain 2.0 and Blockchain 3.0 the limited applications of Blockchain increased exponentially. the efficient implementation of blockchain requires a lot of energy and computational resources, wherein the backend of the consensus algorithm implementation makes it power hungry. In this paper, we have introduced a new programming environment for the implementation of a consensus algorithm with requires less power and computational devices. the RUST-based implementation of the consensus algorithm makes it more power efficient © 2022 IEEE.

关键词： Blockchain

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Multi-task Neural Networks for Pain Intensity Estimation Using Electrocardiogram and Demographic Factors 7th

Multi-task Neural Networks for Pain Intensity Estimation Us...

引用

7th and 8th international conference on information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Gkikas, Stefanos Chatzaki, Chariklia Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Institute of Computer Science Foundation for Research and Technology-Hellas Estavromenos Heraklion70013 Greece

ISBN: (纸本)9783031374951

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. the immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Multi-Network Task Offloading with Improved Recording Neural Network 7

Multi-Network Task Offloading with Improved Recording Neural...

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2022 7th international conference on information Science, computer technology and Transportation, ISCTT 2022

作者： Wang, Longhai Zhang, Luyong School of Information and Communication Engineering Beijing University of Posts and Telecommunication Beijing100876 China

ISBN: (纸本)9783800760565

For the current development of edge computing, it has become a new trend for multiple wireless devices to form a network to connect to the entire system [1]. At the same time, task offloading in this scenario is partially different from traditional edge computing. For multiple systems, how to efficiently allocate tasks has become the primary problem that needs to be solved in this scenario. this paper proposes an improved task offloading algorithm based on neural network. By using neural network to learn the process of task offloading, and at the same time calculate the cost after each decision, the decision that makes the cost decreasing will be recorded, the decision with the original task size is used as the input of the neural network together. Experiments show that the trained neural network can quickly and efficiently obtain the offloading result after each task input while ensuring the lowest delay and energy consumption. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Edge computing

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